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Big Data: 

How Data Analytics 
Is Transforming the World 

Course Guidebook 

Professor Tim Chartier 

Davidson College 


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Copyright © The Teaching Company, 2014 

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Without limiting the rights under copyright reserved above, 
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The Teaching Company. 

Tim Chartier, Ph.D. 

Associate Professor of Mathematics 
and Computer Science 
Davidson College 

P rofessor Tim Chartier is an Associate 
Professor of Mathematics and Computer 
Science at Davidson College. He holds 
a B.S. in Applied Mathematics and an M.S. in 
Computational Mathematics, both from Western 
Michigan University. Professor Chartier received 
his Ph.D. in Applied Mathematics from the University of Colorado Boulder. 
From 2001 to 2003, at the University of Washington, he held a postdoctoral 
position supported by VIGRE, a prestigious program of the National Science 
Foundation that focuses on innovation in mathematics education. 

Professor Chartier is a recipient of a national teaching award from the 
Mathematical Association of America (MAA). He is the author of Math 
Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in 
Computing and coauthor (with Anne Greenbaum) of Numerical Methods: 
Design, Analysis, and Computer Implementation of Algorithms. As a 
researcher, he has worked with both Lawrence Livermore National 
Laboratory and Los Alamos National Laboratory, and his research was 
recognized with an Alfred P. Sloan Research Fellowship. 

Professor Chartier serves on the editorial board for Math Horizons, a 
magazine published by the MAA. He chairs the Advisory Council for the 
National Museum of Mathematics, which opened in 2012 and is the first 
mathematics museum in the United States. In 2014, he was named the 
inaugural Math Ambassador for the MAA. 

Professor Chartier writes for The Huffington Posfs “Science” blog and 
fields mathematical questions for Sport Science program. He also has been 
a resource for a variety of news outlets, including Bloomberg TV, the CBS 
Evening News, National Public Radio, the New York Post, USA TODAY, and 
The New York Times. ■ 

Table of Contents 


Professor Biography.i 

Course Scope.1 


Data Analytics—What’s the “Big” Idea?.5 


Got Data? What Are You Wondering About?.12 


A Mindset for Mastering the Data Deluge.18 


Looking for Patterns—and Causes.24 


Algorithms—Managing Complexity.30 


The Cycle of Data Management.36 


Getting Graphic and Seeing the Data.42 


Preparing Data Is Training for Success.48 


How New Statistics Transform Sports.54 


Political Polls—How Weighted Averaging Wins.60 


Table of Contents 


When Life Is (Almost) Linear—Regression.67 


Training Computers to Think like Humans.73 


Anomalies and Breaking Trends.80 


Simulation—Beyond Data, Beyond Equations.86 


Overfitting—Too Good to Be Truly Useful.93 


Bracketology—The Math of March Madness.100 


Quantifying Quality on the World Wide Web.107 


Watching Words—Sentiment and Text Analysis.114 


Data Compression and Recommendation Systems.121 


Decision Trees—Jump-Start an Analysis.128 


Clustering—The Many Ways to Create Groups.135 


Degrees of Separation and Social Networks.141 


Challenges of Privacy and Security.148 


Table of Contents 


Getting Analytical about the Future.156 


March Mathness Appendix.162 


Big Data: 

How Data Analytics is Transforming the World 


T hanks to data analytics, enormous and increasing amounts of data are 
transforming our world. Within the bits and bytes lies great potential 
to understand our past and predict future events. And this potential 
is being realized. Organizations of all kinds are devoting their energies to 
combing the ever-growing stores of high-quality data. 

This course demonstrates how Google, the United States Postal Service, and 
Visa, among many others, are using new kinds of data, and new tools, to 
improve their operations. Google analyzes connections between web pages, 
a new idea that propelled them ahead of their search engine competitors. 
The U.S. Postal Service uses regression to read handwritten zip codes from 
envelopes, saving millions of dollars in costs. Visa employs techniques 
in anomaly detection to identify fraud—and today can look at all credit 
card data rather than a sampling—and with such advances comes more 
accurate methods. 

This course will help you understand the range of important tools in data 
analytics, as well as how to learn from data sets that interest you. The different 
tools of data analysis serve different purposes. We discuss important issues 
that guide all analysis. We see how dangerously prone humans are to finding 
patterns. We see how the efficiency of algorithms can differ dramatically, 
making some impractical for large data sets. We also discuss the emerging 
and important field that surrounds how to store such large data sets. 

An ever-present issue is how to look at data. Important questions include 
what type of data you have and whether your data is robust enough to 
potentially answer meaningful questions. We discuss how to manage data, 
and then how to graph it. 

Graphing the data, or some portion of it, is a key exploratory step. This, 
if nothing else, familiarizes you with the data. This can help focus your 



questions, because aimless analysis can be like searching haystacks with 
no idea of what counts as a needle. Good graphics can also figure centrally 
in the final presentation of stories found in the data. In between, graphic 
analysis can also produce meaningful results throughout a data analysis. 

A key issue early in the process of data analysis is preparing the data, and 
we see the important step of splitting data. This important but overlooked 
step makes it possible to develop (“train”) a meaningful algorithm that 
produces interesting analysis on some of the data, while holding in reserve 
another part of the data to “test” whether your analysis can be predictive on 
other data. 

This course shares a large variety of success stories in data analysis. While 
interesting in their own right, such examples can serve as models of how to 
work with data. Once you know your data, you must choose how to analyze 
your data. Knowing examples of analysis can guide such decisions. 

Some data allows you to use relatively simple mathematics, such as the 
expected value, which in sports analytics can become the expected number 
of wins in a season based on current team statistics. Such formulas led 
to the success of the Oakland A’s in 2002, as detailed in the book and 
movie Moneyball. 

Is the recency of the data important, with older data being less predictive? We 
see how techniques for weighting and aggregating data from polls allowed 
Nate Silver and others to transform the use of polling data in politics. 

Data analytics draws on tools from statistical analysis, too. Regression, 
for example, can be used to improve handwriting recognition and make 
predictions about the future. 

If you know, in a general way, which variables are important and don’t need 
to assess their relative importance, then artificial intelligence could be a 
good next step. Here, a computer learns how to analyze the data—from the 
data itself. 


Anomaly detection enables credit card companies to detect fraud and 
reduce the risk of fraud. It also enables online gaming companies to detect 
anomalous patterns in play that can indicate fraudulent behavior. 

When data involves vast numbers of possibilities, analysis can turn to 
simulating a phenomenon on a computer. Such techniques enable the 
aerodynamics of cars to be tested before a prototype is constructed and lead 
to the special effects we see in movies and scientific visualizations. 

The ability to determine which variables are influential is quite important. In 
fact, including too many variables can lead to the pitfall known as overfitting, 
where methods may perform stunningly well on past data but are terrible at 
predicting future data. 

Data mining, which involves looking for meaning within larger data sets, 
often makes use of linear algebra. This mathematical tool starts like high 
school algebra, except we put our equations into a matrix form. From there, 
performing even a complex matrix analysis can be as simple as pushing 
a button on a computer. So, the key becomes understanding what we are 
doing. Linear algebra lies at the core of Google’s ability to rank web pages, 
the determination of schedule strength for a sports team to better predict 
future, and the entire field of data compression. 

Another approach early in an analysis, if the data is looking at a single 
“root” variable, is decision trees, which split data in order to predict disease, 
for example. Sometimes, decision trees suffice as a stand-alone analytical 
tool. Other times, they can be used like a sieve, to prepare the data for other 
methods, thereby jump-starting the analysis. And when no single master 
variable is targeted, many other methods for clustering are used—for 
example, Netflix and many other companies profile their customers. 

We can also study data about relationships, allowing one to determine who is 
at the center of Hollywood or professional baseball, along with the validity 
of the claim that everyone on our planet is connected by six people, or by six 
degrees of separation. 



A key insight we keep in mind amid all the hype about “big data” is that 
small data sets continue to offer meaningful insights. Beware of thinking 
that you need more data to get results; we see how more data can make the 
analysis more difficult and unwieldy. Returning to the haystack analogy, we 
want to avoid making a bigger haystack without including any more needles. 

Thinking like a data analyst also involves realizing that previous ideas can 
be extended to other applications. Conversely, no single tool answers all 
questions equally: A different tool may tell a different story. 

Our modern data deluge offers a treasure trove of exciting opportunities to 
unveil insight into our world. We can understand how data analytics has 
already transformed many current practices, as well as how we can better 
navigate further changes into the future. ■ 


Data Analytics—What’s the “Big” Idea? 

Lecture 1 

T he field of data analysis relates to, and impacts, our world in 
unprecedented ways. Right now, millions, even billions, of computers 
are collecting data. From smartphones and tablets to laptops and 
even supercomputers, data is an ever-present and growing part of our lives. 
What makes data analytics so powerful are the fundamental techniques 
you will learn for analyzing data sets. Data analysis is a set of existing and 
ever-developing tools, but it is also a mindset. It’s a way of improving our 
ability to ask questions, and it’s an expectation that data can make possible 
new answers. 

Big Data 

• A 2012 Digital Universe study estimated that the global volume 
of digital data stored and managed in 2010 was over a trillion 
gigabytes—which is equivalent to a billion terabytes (so, less 
than one terabyte per person at that point), a million petabytes, a 
thousand exabytes, or a zettabyte. That was in 2010, and the number 
was predicted to double every year, reaching 40 trillion gigabytes 
by the year 2020. 

• Those numbers are for all the data—no one person or computer 
has all the data that’s distributed over all the computing devices 
everywhere. Still, even individual data sets are huge. In fact, so 
many applications are creating data sets that are so big that the 
ways we traditionally have analyzed data sometimes do not work. 

• Indeed, the ideas we have today might not solve the questions we 
have for the data tomorrow. As more and more data is collected, and 
as the technology we use to collect that data changes, new questions 
will arise, which may mean we need new ways to analyze the data 
to gain insight. 


Lecture 1: Data Analytics—What’s the “Big” Idea? 

• Data analysis is a fairly new combination of applied mathematics 
and computer science, available in ways that would have been 
difficult to imagine a few decades ago and inconceivable 100 years 
ago. Why? A lot of it has to do with data. And this flood of new data 
is being organized, analyzed, and put to use. 

• For example, online companies like Amazon, Netflix, and Pandora 
are gathering rating after rating from millions of people and then 
putting all of that data to use. Similar transformations are taking 
place in politics, sports, health care, finance, entertainment, science, 
industry, and many more realms. 

• We are collecting data as never before, and that creates new kinds 
of opportunities and challenges. In fact, so many applications are 
creating data sets that are so big that the ways we traditionally have 
analyzed data don’t work. Indeed, the ideas we have today might 
not solve the questions we have for the data of tomorrow. This is 
the idea behind the term “big data”—where the sheer size of large 
data sets can force us to come up with new methods we didn’t need 
for smaller data sets. 

The Three Vs of Big Data 

• Big data is often defined as having three Vs: volume, velocity, 
and variety. First, in terms of volume, which would you say is 
bigger: the complete works of Shakespeare or an ordinary DVD? 
The complete works of Shakespeare fit in a big book, or roughly 
10 million bytes. But any DVD—or any digital camera, for that 
matter—will hold upward of 4 gigabytes, which is 4 billion bytes. A 
DVD is 400 times bigger. 

• And data is not merely stored: We access a lot of data over and over. 
Google alone returns to the web every day to process another 20 
petabytes—which is equal to 20,000 terabytes, 20 million gigabytes, 
or 20 quadrillion bytes. Google’s daily processing gets us to 1 
exabyte every 50 days, and 250 days of Google processing may be 
equivalent to all the words ever spoken by mankind to date, which 


have been estimated at 5 
exabytes. And nearly 1,000 
times bigger is the entire 
content of the World Wide Web, 
estimated at upward of 1 
zettabyte, which is 1 trillion 
gigabytes. That’s 100 million 
times larger than the Library of 
Congress. And of course, there 
is a great deal more that is not 
on the web. 

• Second is the velocity of data. 

Not only is there a lot of data, 
but it is also coming at very 
high rates. High-speed Internet 
connections offer speeds 
1,000 times faster than dial-up 
modems connected by ordinary 
phone lines. Every minute of the day, YouTube users upload 72 
hours of new video content. Every minute, in the United States 
alone, there are 100,000 credit card transactions, Google receives 
over two million search queries, and 200 million e-mail messages 
are sent. 

• Third, there is variety. One reason for this can stem from the need 
to look at historical data. But data today may be more complete 
than data of yesterday. We stand in a data deluge that is showering 
large volumes of data at high velocities with a lot of variety. With 
all this data comes information, and with that information comes 
the potential for innovation. 

• We all have immense amounts of data available to us every day. 
Search engines almost instantly return information on what can 
seem like a boundless array of topics. For millennia, humans have 
relied an each other to recall information. The Internet is changing 
that and how we perceive and recall details in the world. 

The Internet is revolutionizing the 
ways we send and receive data. 


© Thomas Northcut/Photodisc/Thinkstock. 

Lecture 1: Data Analytics—What’s the “Big” Idea? 

• Human beings tend to distribute information through what is called 
a transactive memory system, and we used to do this by asking each 
other. Now, we also have lots of transactions with smartphones and 
other computers. They can even talk to us. 

• In a study covered in Scientific American, Daniel Wegner and Adrian 
Ward discuss how the Internet can deliver information quicker than 
our own memories can. Have you tried to remember something 
and meanwhile a friend 
uses a smartphone to 
get the answer? In a 
sense, the Internet is an 
external hard drive for 
our memories. 

• So, we have a lot of 
data, with more coming. 

What works today may 
not work tomorrow, 
and the questions of 
today may be answered 
only to springboard 
tomorrow’s ponderings. But most of all, within the data can exist 
insight. We aren’t just interested in the data; we are looking at data 
analysis, and we want to learn something valuable that we didn’t 
already know. 

• You don’t need large data sets to pose computationally intensive 
problems. And even on a small scale, such problems can be too 
difficult to allow for optimal solutions. 

• Data analysis doesn’t always involve exploring a data set that 
is given. Sometimes, questions arise and data hasn’t yet been 
gathered. Then, the key is knowing what question to ask and what 
data to collect. 

Smartphones are becoming increasingly 
able to complete transactions that 
would be difficult for our brains. 

© ponsulak/iStock/Thinkstock. 

• How big and what’s big enough depends, in part, on what you are 
asking and how much data you can handle. Then, you must consider 
how you can approach the question. 

Misconceptions about Data Analysis 

• There are several misconceptions about data analysis. First, 
data analysis gives you an answer, not the answer. In general, 
data analysis cannot make perfect predictions; instead, it might 
predict better than we usually could without it. There is more than 
one answer. Much of life is too random and chaotic to capture 
everything—but it’s more than that. Unlike math, data analytics 
does not get rid of all the messiness. So, you create an answer 
anyway and try to glean what truths and insights it offers. But it’s 
not the only answer. 

• Second, data analysis does involve your intuition as a data analyst. 
You are not simply number crunching. If you build a model and 
create results that go against anything anyone previously has found, 
it is likely that your model has an error. 

• Third, there is no single best tool or method. In fact, many times, 
part of the art and science of data analysis is figuring out which 
method to use. And sometimes, you don’t know. But there are some 
methods that are important to try before others. They may or may 
not work, and sometimes you simply won’t know, but you can 
learn things about your data and viable paths to a solution by trying 
those methods. 

• Fourth, you do not always have the data you need in the way you 
need it. Just having the data is not enough. Sometimes, you have 
the data, but it may not be in the form you need to process it. It may 
have errors, may be incomplete, or may be composed of different 
data sets that have to be merged. And sometimes just getting data 
into the right format is a big deal. 


Lecture 1: Data Analytics—What’s the “Big” Idea? 

• Fifth, not all data is equally available. It is true that some data 
sets are easy to find. They already exist on the Internet. You can 
download them and immediately begin analyzing the data. But 
other pieces of data may not be as easily available. It doesn’t mean 
that you can’t get it. It’s out there, but you need to figure out how 
to grab it. 

• Sixth, while an insight or approach may add value, it may not 
add enough value. Not every new and interesting insight is worth 
the time or effort needed to integrate it into existing work. And 
no insight is totally new: If everything is new, then something is 
probably wrong. 

Suggested Reading 

Davenport, Big Data at Work. 
Paulos, Innumeracy. 


1. One way to open your mind to the prevalence of data is to simply stay 
attuned to your use of it. As you rate films or songs, use a credit card, 
make a phone call, or update your status on Facebook, think about the 
data being created. It is also interesting to look for news stories on data 
and to take note when new sources of data are available. 

2. If you pay any bills online, look for the availability to download your 
own data. Whether you can or do, what might you analyze? What 
might you be able to find or see? How much data do you expect to be in 
the file? 


3. An important part of this course is learning tools of data analysis and 
applying them to areas of your personal interest. What interests you? 
Do you want to improve your exercise? Do you want to have a better 
sense of how you use your time? Furthermore, think about areas of our 
world where data is still unruly. What ideas do you have that might tame 
the data and make it more manageable? You may or may not be able to 
implement such ideas, but beginning to look at the world in this way 
will prepare you to see the tools we learn as methods to answering those 
questions in the data deluge. 


Lecture 2: Got Data? What Are You Wondering About? 

Got Data? What Are You Wondering About? 

Lecture 2 

I n very broad strokes, there are three stages of data analysis: collecting 
the data, analyzing the data (which, if possible, includes visualizing 
the data), and becoming a data collector—not of everything, but in a 
purposeful way. After all, no one has time to gather and analyze all data of 
potential interest, just as no one has time to read every worthwhile book. 
In fact, you have a better chance of being able to analyze every book that 
interests you than of being able to analyze the quantity and variety of all 
types of data available today. 

Collecting Data 

• Data analysis is not just about large organizations and large data 
sets. Data analytics is also about individual people. Your financial 
details can be monitored and analyzed as never before. Your 
medical data and history can be organized to give you, as well as 
professional caregivers, unprecedented insight. 

• It is now much easier to track, adjust, and understand any aspect 
of everyday life: including eating, sleep, activity levels, moods, 
movements, habits, communications, and so on. These are exciting 
changes. They make possible new fields such as personalized 
medicine, lifelogging, and personal analytics of all kinds. And they 
are coming together for some of the same reasons that data analytics 
as a whole is taking off. 

• First, the wide range of technologies and methods available to 
large organizations are also increasingly available to individuals. 
Virtually all the tools you will learn in this course—such as 
grouping data into clusters, or finding correlations with regression, 
or displaying data with infographics—can also be used on your 
own data. You can use the ideas and techniques of data analytics to 
live a healthier life, save money, be a better coach to others in many 
areas of life, and so on. 


• Second, large organizations are already accumulating more and 
more data about you and your world. So, they are, in effect, doing 
a lot of heavy lifting on your behalf, if you choose to access and 
make use of the data thereby accumulated. Even so, there is plenty 
of personal data analysis you can do, even without needing access 
to large data sets or sophisticated tools. 

• There is a lot of data that could be kept on whatever you want to 
analyze. But the possibilities, and the realities, of having a lot of 
data can also be overwhelming. Keep in mind that even simple 
analysis, without extensive data, can give insight. 

• A data analysis cycle involves collecting, analyzing, questioning, 
making a change, and then reanalyzing the data to understand what 
is happening. 

• Today, there are many digital 
devices that aid with collecting 
exercise data, for example. The 
devices may keep track of how 
far you ran, hiked, or swam. They 
can break down your exercise 
by the mile or minute, and some 
also give you some analysis. A 
device may log your steps, or how 
many calories you’ve burned, 
or the amount of time you’ve 
been idle during the day, and 
sometimes your location, whether 
you are climbing, your heart 
rate, and so on. Then, they can 
connect to applications on your 
computer or smartphone and give 
you feedback. 

Smartphones are now able to 
log, store, and analyze lots of 
data, including running data. 


© AmmentorpDK/iStock/Thinkstock. 

Lecture 2: Got Data? What Are You Wondering About? 

• Why bother with all that data? It can offer insight. If you like to 
walk, does it make a difference where you walk? If you walk on 
a trail versus pavement, or if you walk one scenic route versus 
another, do these make differences? It isn’t always that you need to 
change something; sometimes, it is simply a matter of having the 
knowledge to be informed about your choice. 

• But while gathering data is worthwhile, be careful not to assume 
that data automatically means insight. In particular, just buying a 
device and collecting data does not mean that you’ll gain insight. 
Many companies have made that mistake—collecting evermore 
data to try to take advantage of data analytics. They have more data. 
But, again, that doesn’t necessarily mean that they’ll gain insight, 
even if they attempt an analysis. 

• In 2013, The Wall Street Journal reported that 44 percent of 
information technology professionals said they had worked on 
big-data initiatives that got scrapped. A major reason for so many 
false starts is that data is being collected merely in the hope that 
it turns out to be useful once analyzed. In the same Wall Street 
Journal article, Dalian Shirazi, founder of Radius Intelligence Inc., 
describes the problem as “haystacks without needles.” He notes 
that companies “don’t know what they’re looking for, because they 
think big data will solve the problem.” 

• On the other hand, once you have a goal, or a question you want 
to answer, you’ll have much more success, both immediately and 
over the longer term. In fact, once you know what you are trying 
to learn, you can often think quite creatively about how to collect 
the data. 

• So, having a clear goal makes a huge difference. Instead of just 
piling up data in the hopes that insight will pop out, having a clear 
goal guides you into gathering data that can produce insight. And 
with a bit of creativity, gathering the data may be much less onerous 
than you think. In fact, sometimes you already have data, but you 
may not realize that you do. 


Analyzing Data 

• When comparing data for two things in an attempt to analyze the 
data, you could compare two projects at work, two schools, two 
recipes, two vehicles, or two vacations—really anything that 
interests you. 

• Or you can compare just one case to typical values for that one case. 
This is what a student learning analytics did at Mercer College. He 
was taking a class from Dr. Julie Beier, who asked her students to 
track personal data. The student was concerned about his aunt, who 
used the free clinic in town and had diabetes. The student felt that 
her medication was incorrectly calibrated. So, he kept track of her 
glucose levels, which she was already measuring. 

• The student gathered this data and compared it with acceptable 
values, and it looked high. He could have easily stopped there. In 
fact, in data analytics, that’s often where we do stop. But he even 
used a statistical test to see how likely it was that the readings 
would be that high, just by chance. That’s called hypothesis testing, 
and it’s sort of statistical inference traditionally called in when 
your sample is only a tiny part of a large population. But in data 
analytics, we are often studying a whole population or zooming in 
on a specific case. 

• The main point, from the perspective of data analytics, is that he 
collected the data and compared to see what it meant. With his 
newfound information, the student and his aunt walked into the 
doctor’s office with the data and conclusions. The result was a 
change in the aunt’s medication. What is needed is data aimed at 
answering a question. 

Becoming a Data Collector 

• The nature of data analysis is that we don’t have everything, but we 
can work with the data we do have to learn and gain insight. And 
the process of questioning is important. Data analytics offers new 
insight, but not all at once. With insight comes knowledge but also 
the potential to leam more. So, be prepared. Once data helps you 


Lecture 2: Got Data? What Are You Wondering About? 

answer one question, you are likely to have another, and you will 
have to go back for more data. But you can keep digging, learning, 
and improving your decisions along the way. 

• So, this is what it’s like to begin as a data analyst. Collect data 
associated with a question that interests you. Keep your own 
interests in mind. These days, there are various ways to share 
your data, giving you and others more opportunities to learn from 
whatever you gather. Today, many devices can directly display and 
share the data, not only with your own computer but even with 
social media. 

• What do you care about? If you see a connection to your life, jot it 
down so you can look at it. And then think about how to gather the 
necessary data. If you have it, great; if not, think about gathering it. 
Remember that it doesn’t have to be a lot of data. Start somewhere. 
Also, look for opportunities to share, which is fun and helps you 
learn more from your data. 

• Next, as you learn the tools of data analytics, think about which 
tools might apply to the data and address questions you have. Keep 
in mind that you may want to try a few methods so that you get 
different insights on the data. And remember that visualizing data 
often helps a lot. Whether the data is big or small, visualization can 
help you see when your sleeping patterns changed, for example, or 
what is happening during a sport or other physical activity. 

Suggested Reading 

Gray and Bounegru, The Data Journalism Handbook. 
Russell, Mining the Social Web. 



1. A key to data analytics is data. Papers, such as the Guardian, have data 
sites with downloadable data related to their articles. Look for such sites 
and see what data sets interest you. 

2. The following are a few other data repositories for you to explore and 
begin thinking of questions that interest you. Having your questions can 
help you frame what you might do with the tools we will learn. 


Lecture 3: A Mindset for Mastering the Data Deluge 

A Mindset for Mastering the Data Deluge 

Lecture 3 

W e stand within a data explosion of sorts. Organizations talk about 
trying to drink from a “fire hose” of information. Commentators 
refer to a “data deluge.” But there is no need to drown in data. In 
this lecture, you will learn how data analysts of many kinds think about their 
data—the amount of data, the types of data, what constraints there may be on 
an analysis, and what data is not needed. In this way, you will learn how the 
deluge can be put to work, answering questions you have by developing the 
mindset of a data analyst. 

The Size of Data 

• There is a lot of data, and it can be difficult to wrap one’s mind 
around the huge numbers. But it is possible. As data analysts, this 
is what we do. With data analysis, data can be managed in a way 
that’s both timely and useful. 

• Keep in mind that advances in storage play into this. Consider 
50 GB, which is about the amount of storage on a Blu-ray disc. 
This would hold the textual content of just about a quarter of a 
million books. That’s simply a disc you might have laying around. 
Storage capacity of a high-end drive from companies like Seagate 
or Western Digital can hold 5 terabytes or more. A terabyte is 
1,000 gigabytes. 

• With all this data, we begin to see why there began to be a lot of 
talk about big data. But without analysis, the data is essentially a lot 
of Is and Os. If you can’t analyze it, it may not be helpful. Proper 
analysis can enable one to gain insight even with big data. But “big” 
is a relative tern. We may call something “big” and only mean it in 
the context of data that in some other arena might seem small. 


• Our sense of size changes with time, too. The Apollo 11 computers 
were fast and stored a lot of data for the time. Later, during the 
time of floppy discs, holding 1.44 MB, a gigabyte seemed about as 
remote as the petabyte or exabyte are for many of us today. 

• To learn about size, we need to learn about how we measure and 
have a sense of what each measurement means. 

o A bit is a single binary digit. It equals 0 or 1—“on” or “off’ in 
the hardware. A bit is a single character of text. 

o Eight bits make up a byte. Ten bytes is a written word. 

o One kilobyte is equal to 1,000 bytes and equals a short 
paragraph. Two kilobytes is equal to a typewritten page. 

o A megabyte is equal to 1,000 kilobytes and equals a short 
novel. Ten megabytes is enough for the complete works 
of Shakespeare. 

o Seven minutes of high-definition television video is 1 gigabyte, 
or 1,000 megabytes. A DVD can hold from 1 to 15 gigabytes; 
Blu-ray disks can hold 50 to 100 gigabytes. 

o One thousand gigabytes equates to 1 terabyte. Ten terabytes 
equals all the text information in books held by the U.S. 
Library of Congress, and 400 terabytes might be sufficient to 
hold all the books ever written. 

o A thousand terabytes is equal to 1 petabyte, or 10 million four- 
drawer filing cabinets filled with text. 

o One thousand petabytes is 1 exabyte. All the words ever spoken 
by mankind one decade into the 21 st century may have equaled 
about 5 exabytes. 


Lecture 3: A Mindset for Mastering the Data Deluge 


One thousand exabytes equals 1 zettabyte. This is roughly the 
scale of the entire World Wide Web, which may be doubling in 
size every 18 months or so, with 1 zettabyte reached perhaps in 
the year 2011. 

o One thousand zettabytes equals 1 yottabyte, which is 1 
quadrillion gigabytes. Using a standard broadband connection, 
it would take you 11 trillion years to download a yottabyte. 
For storage, 1 million large data centers would be roughly 
1 yottabyte. 

Analyzing Big Data 

• Many people are working with big data and trying to analyze it. 
For example, NASA has big data on a scale that can challenge 
current and future data management practice. NASA has over 100 
missions concurrently happening. Data is continually streaming 
from spacecraft on Earth and in space, faster than they can store, 
manage, and interpret it. 

• One thing about some of the largest data sets is that they are often 
being analyzed to find one specific thing. But many data sets 
are much more complex. In fact, when things get too big, you 
sometimes peel off part of your data to make it more manageable. 

• But excluding some of the data is important on much smaller scales, 
too. Flow much data can you omit from a large data set and still be 
okay for the question you are investigating? Moreover, do you lose 
insight if you omit? Could excluding such data be relevant to issues 
that interest you, but you simply don’t know it yet? Such challenges 
are inherent in data analysis, making it both more difficult and 
more interesting. 

• In fact, a concern with the term “big data” is that although you 
can do amazing things with big-data sets, the field is not merely 
about a few big businesses. The same lessons can apply to all of us. 
Sometimes, the data is already available. The trick is recognizing 
how much to use and how to use it. 


• Sometimes, relevant information comes from returning to the 
same places many times. In other situations, we might not even 
know what we don’t know. Gus Hunt of the Central Intelligence 
Agency stated this really well in a 2013 talk. He noted, “The value 
of any information is only known when you can connect it with 
something else which arrives at a future point in time.” We want 
to connect the dots, but we may not yet have the data that contains 
the dot to connect. So, this leads to efforts to collect and hang 
on to everything. 

• Furthermore, data from the past may not have been stored in a usable 
way. So, part of the data explosion is having the data today and for 
tomorrow. The cost of a gigabyte in the 1980s was about a million 
dollars. So, a smartphone with 16 gigabytes of memory would be a 
16-million-dollar device. Today, someone might comment that 16 
gigabytes really isn’t that much memory. This is why yesterday’s 
data may not have been stored, or may not have been stored in a 
suitable format, compared to what can be stored today. 

Structured versus Unstructured Data 

• A common way to categorize data is into two types: structured data 
and unstructured data. Just this level of categorization can help you 
learn more about your data—and 
can even help you think about how 
you might approach the data. 

First, structured data is the type 
of data that many people are most 
accustomed to dealing with, or 
thinking of, as data. Your list of 
contacts (with addresses, phone 
numbers, and e-mail addresses) and 
recipes are examples of structured 
data. It can be a bit surprising that 
most experts agree that structured 
data accounts for only about 20 
percent of the data out there. 

Lists of addresses, phone 
numbers, and e-mail 
addresses are examples of 
structured data. 



Lecture 3: A Mindset for Mastering the Data Deluge 

• There are two sources of structured data: computer-generated 
data and human-generated data. The boundary between computer¬ 
generated and human-generated data is not fixed. For example, a 
doctor may personally input medical information into a case file, 
but that might appear in combination with data read automatically 
from routine scans or computer-based lab work. 

• Second, unstructured data doesn’t follow a prespecified format. 
While perhaps 80 percent of identified data comes in this form, 
until recently we didn’t have mechanisms for analyzing it. In fact, 
there were even problems just storing it, or storing it in a way that 
could be readily accessed. 

• Scientific data often is unstructured and can be anything from 
seismic imagery to atmospheric data. Everyday life also produces 
a lot of unstructured data. There are e-mails, text documents, text 
messages, and updates to sites like Facebook, Twitter, or Linkedln. 
There is also web site content that’s added to video and photography 
sites like YouTube or Instagram. 

• If data is structured, it is more likely that a method, possibly 
around for some time, has been developed to analyze it. If data 
is unstructured, this is much less likely. A million records in a 
structured database are much easier to analyze than a million videos 
on YouTube. Unstructured data can still have some structure, but 
overall, the data is much more unstructured. 

• Part of what it means to think like a data analyst is deciding what 
to analyze and how. This is always important. In addition to getting 
the data and having a sense of what form it comes in, you need to 
consider how quickly it comes in. Is the data going to come in real 
time? If so, how quickly will you need to analyze it? 


• Today, knowing where data is coming from, how much of it is 
coming, and how quickly you are going to need to analyze it are all 
very real and very important questions. The amount of data needed 
for a problem depends in part on what you are asking and how 
much data you can handle. Then, you must consider how you can 
approach the question. 

Suggested Reading 

Brenkus, The Perfection Point. 
Mayer-Schonberger and Cukier, Big Data. 


1. An interesting exercise is to simply look for data sets. What is available 
and what is not, at least easily? Then, several months later, you may 
want to look again and see what may have changed. The landscape 
of available data is always changing, and keeping this in mind is very 
important as a data analyst. 

2. What data do you have? What data does someone else have who 
might be willing to share it? Students can e-mail campus groups to ask 
questions about their data they are interested in. 

3. When you hear about data or people using data to come to conclusions, 
think about what you might do. Even if you don’t have the data 
available, simply thinking about what you would do will improve your 
ability to work with the data you do and will have. You’ll be honing 
your data analyst mindset. 


Lecture 4: Looking for Patterns—and Causes 

Looking for Patterns—and Causes 

Lecture 4 

I t’s in our nature to find connections—real or not—and this ability is 
what lets us take surprising correlations from data analysis and find 
impressive connections. Beware of just rushing in where angels fear 
to tread. Some of those connections are real. And because of that, we will 
continue to see them. Top athletes, investors, and researchers will continue 
to look for patterns to improve their performance. We all love an interesting 
pattern, especially if it comes with a plausible story. The difference in good 
data analysis is that we don’t stop there. Finding a pattern is a great start, but 
it’s also just a beginning. 

Pareidolia and Seeing Patterns 

• We organize information into patterns all the time; our mind has 
a way of organizing the data that we see. This type of thinking 
is a part of how we think. In fact, we can also make up patterns, 
seeing things that are not there—for example, when we look at 
clouds, or inkblots, or other random shapes. Psychologists call this 
pareidolia, our ability to turn a vague visual into an image that we 
find meaningful. 

• We do this with what we see, and we also do this in how we think 
about cause and effect. Professional athletes, for example, often 
look for patterns of behavior that lead to success. They are under 
constant pressure to perform at a high level, so if a player finds 
something that meets success, he or she repeats it. Maybe it helps; 
maybe it doesn’t. But this can be taken to an extreme. 

• In basketball, Michael Jordan, who led the Chicago Bulls to six 
NBA championships, had his rituals. The five-time MVP wore 
his University of North Carolina (UNC) shorts under his uniform 
in every game. Jordan led UNC to the NCAA Championships in 
1982—which was a really good outcome—so he kept wearing 


that lucky pair. Players sometimes see a correlation between their 
success and some activity, so they repeat it. 

• We all look for correlations. When is a pattern real, and when is 
it merely spurious or imagined? For example, increased ice cream 
sales correspond to increased shark attacks. Correlation picks up 
that two things have a certain pattern of happening together: more 
ice cream sales and more shark attacks. However, there is a well- 
known aphorism in statistics: Correlation doesn’t mean causation. 
It could be that the connection is simply a random association in 
your data. 

• But if there is a connection, many other things can cause the 
connection. The two factors may themselves not be particularly 
connected but, instead, be connected to another factor. For example, 
maybe weather is warmer in a particular area at the time when 
sharks tend to migrate in that area. Maybe the warmer weather 
causes an increase in the presence of sharks and an increase in 
people eating ice cream. Ice cream consumption and shark attacks 
just happen to be correlated, but one does not cause the other. 

• A published medical study reported that women who received 
hormone replacement therapy were less likely to have coronary 
heart disease. It turns out that more affluent women had access to 
the hormones, and that same female population had better health 
habits and better access to all kinds of health care, which was 
probably a much better indicator of less heart disease. 

• Such research results can have worldwide effects—seemingly 
positive effects at first, but actually quite harmful ones. News of 
a big result can spread quickly, but if found wrong, the impact of 
such news can be difficult to reverse. 

• The Wall Street Journal reported in 2011 that retractions of scientific 
studies were surging. This can put patients at risk, and millions of 
dollars’ in private and government funding can go to waste. Some 
research is retracted due to researchers unethically fabricating 


Lecture 4: Looking for Patterns—and Causes 

results or for plagiarism, but in other cases, people find connections 
that do not offer the level of insight touted. And the increasingly 
powerful tools for data analysis and data visualization now make it 
easier then ever to “over-present” the results of a study. It is very 
important to keep in mind that our tendency is to essentially 
overexplain and overpredict what we find. 

Indeed, scientists are finding this to be hardwired into the human 
brain. Psychologists have long known that if rats or pigeons knew 
what the NASDAQ is, they might be better investors than most 
humans are. In many ways, animals are better predictors than people 
when random events are involved. People keep looking for higher- 
order patterns and thinking 
they see one. Attempts to use 
our higher intelligence leads 
people to score lower than rats 
and pigeons on certain types 
of tasks. 

We can also look too hard for 
patterns in the randomness 
of financial markets. A few 
accurate predictions on the 
market, and an analyst can 
seem like an expert. But how 
will the person do over the 
long run? 

We have a tendency to 
overlook randomness. If you 
just flipped a fair coin and got 
heads 13 times in a row, what 
do you think you would flip next? Does some part of you think 
that it is more likely to be tails? This type of thinking is common 
enough that it has a name: the gambler’s fallacy. It’s what can keep 
us at the tables in Las Vegas or pulling the slot machine levers. 

The gambler's fallacy is what 
keeps people pulling slot machine 
levers over and over again. 


• However, there can be a lot at stake in such thinking. Interestingly, 
people don’t always pick the option with the highest probability of 
success over time. Blit humans will move into that type of thinking 
when the outcomes really matter and the stakes are high. 

• This can play into financial decisions. If we are determined that 
there is a pattern where there isn’t one, we could be making the 
wrong decision. In such a way, we could actually make our worst 
financial decisions on small amounts of money, but before long, 
that can equate to a larger decision. So, one way to look at that 
type of thing is to convince yourself that there is no small or casual 
investment when it comes to finance. 

Apophenia and Randomania 

• Be careful thinking that there aren’t patterns. There are. And if we 
find them, the consequences can be very powerful. But the fact that 
there is a correlation doesn’t mean that the correlation will predict, 
or will continue to predict, as well as before. Again, correlation 
doesn’t necessarily mean causation. We simply have a tendency to 
think in that way. It’s like it is hardwired into us. 

• It was to our ancestors’ advantage to see patterns. If you saw a bush 
shake and a tiger jump out, then it might behoove you to keep that 
in mind: Even if for the next 100 times that shaking is the wind and 
not a tiger, on the 100 th time, if a tiger jumps out, that’s an important 
connection to notice! There is a correlation. A tiger could rustle 
a bush before pouncing, but remember, that doesn’t mean that a 
bush’s rustle is a tiger. 

• There is a name for this. Apophenia is the experience of seeing 
patterns or connections in random or meaningless data. The name is 
attributed to Klaus Conrad and has come to represent our tendency 
to see patterns in random information. But Conrad was actually 
studying schizophrenia in the late 1950s. He used the term to 
characterize the onset of delusional thinking in psychosis. In 2008, 
Michael Shermer coined the word “patternicity,” defining it as “the 
tendency to find meaningful patterns in meaningless noise.” 


Lecture 4: Looking for Patterns—and Causes 

• On the other end of the spectrum is randomania, which is where 
events with patterned data are attributed to nothing more than 
chance probability. This happens when we overlook patterns, 
instead saying, “It was just totally random.” But the most common 
reason we overlook patterned data is that we already have some 
other pattern in mind, whether it is a real connection or not. 

• In his book On the Origin of Stories, Brian Boyd explains why 
we tell stories and how our minds are shaped to understand them. 
He argues that art is a specifically human adaptation. Boyd further 
connects art and storytelling to the evolutionary understanding of 
human nature. 

• For Boyd, art offers tangible advantages for human survival. 
Making pictures and telling stories has sharpened our social 
cognition, encouraged cooperation, and fostered creativity. How 
can this help us from an evolutionary point of view? Humans 
depend not just on physical skills but even more on mental power. 
We dominate that cognitive niche, and as such, skills that enhance it 
can aid us. Looking for patterns from that point of view aids us, and 
when we see patterns, we create meaning and may even tell a story. 

• Whatever the case, we do have a tendency to look for patterns. And 
that can be a real problem and a real strength in data analysis. The 
important part is to realize and recognize that we may unearth a 
pattern in data analysis. It may even be surprising. But even if we 
can offer a possible explanation, that still doesn’t mean that we 
have found something meaningful. 

• In data analysis, we look for patterns in the data. We as people are 
good at it, but sometimes, we are good at finding something that 
isn’t there. It’s an ever-present balance. As you look for and find 
correlated data, be careful. 

o First, look in both directions, and see if you can think of why 
one might cause the other. Maybe causality is there, but maybe 
it goes in the opposite direction from what you expected. 


o Second, like warm weather explaining shark attacks and ice 
cream consumption, check to see whether something else 
offers a better explanation. 

o Lastly, always keep in mind that it might just be your hardwired 
ability that’s leading you to expect something that isn’t there. 

Suggested Reading 

Boyd, On the Origin of Stories. 
Devlin, The Math Instinct. 


1. Consider the following series of heads and tails. 



Which is random? The first series is a series of actual flips of a quarter. 
The second is one that was made up in trying to keep the number of 
heads and tails equal. Often, people will think that a random set 
of flips isn’t random, because often a number of heads or tails will 
consecutively fall. 

2. Visit Google flu trends at and see 
what it is predicting for your area or for an area of interest. Have you 
recently conducted a search on the flu? When you do, what do you 
search on? 

3. To see more examples of domino mosaics, visit Robert Bosch’s web site 


Lecture 5: Algorithms—Managing Complexity 

Algorithms—Managing Complexity 

Lecture 5 

D ata sets are getting bigger, so a fundamental aspect of working with 
data sets today is ensuring that you use methods that can sift through 
them quickly and efficiently. In this lecture, you will learn about a 
core issue of computing: complexity. Managing complexity is an important 
part of computer science and plays an important role in data analytics. 
You will discover that algorithms are the key to managing complexity. It 
is algorithms that can make one person or company’s intractable problem 
become another’s wave of innovation. 

Algorithms and Complexity Theory 

• Billions of dollars are spent with credit card numbers flying through 
the World Wide Web to make online purchases. When you make 
such a purchase, you want to be on a secure site. What makes it 
secure? The data is 
encrypted. And then 
it’s decrypted by the 
receiver, so clearly 
it can be decrypted. 

Broadly speaking, 
encryption techniques 
are based on factoring 
really huge numbers— 
such as 10 75 . 

• How do we know that when y° u make online Purchases on a 

secure site, the data is encrypted, and then 
someone isn t going to jt is decrypted by the receiver . 

be able to factor such a 

number on a computer 

or some large network of computers? Computers keep increasing in 
speed. Maybe tomorrow there will be a computer that’s fast enough, 
and suddenly Internet sales are insecure. But a computer simply 
cannot move at these speeds; having a computer even 1,000 times 



faster isn’t going to be enough to crack the code. The ideas that 
ensure the safety of such methods also determine how large a data 
set someone can look at, or if someone’s data analysis technique 
can be done in real time. 

• Computer scientists, long before the massive data sets of today, 
have studied how fast algorithms work. This is called complexity 
theory. It can help you compare algorithms and know if one will 
work as problems grow. For example, it can tell you if what you 
are doing will work if your company suddenly spikes in users. 
If you go from 150 users to half a million, will things still be 
done efficiently? 

• Complexity theory can help us know how big of problems we can 
analyze. If we double the problem, is it going to take just a tad 
longer, or twice as long—or more? 

• Can we write the sum of 1 through 100 in a clean way? There is 
a famous story that this problem was given to a primary school 
class in the late 1700s as punishment. The young mathematician 
Carl Friedrich Gauss was a child in the class. Fie later became a 
prominent mathematician with work that is used today in many 
fields of mathematics. 

• Gauss saw a pattern. Take the numbers 1 through 100, and under 
them write the numbers 100 through 1. 

100 + 99 + 98 + ... + 1 

1 + 2 + 3 + ... + 100 

101 + 101 + 101 + ... + 101 

• Next, add each column of numbers. So, you are adding 100 and 
1, which is 101. Then, you add 99 and 2, which is 101. That’s the 
key—you will get 101 in every case. And there are 100 of them. So, 
the sum of 1 through 100 and 100 through 1 is 100 times 101. This 
is twice what we need, so summing 1 through 100 is 50 x 101. 


Lecture 5: Algorithms—Managing Complexity 

• What if we added the integers between 1 and 500? This would 
equal 250 x 501. The formula in general for adding the integers 
between 1 and n is 

n[n + l) 

2 ' 

Exponential Growth 

• Suppose that a highly contagious virus hits. It starts with one person. 
The next day, that person and someone else are sick. The next day, 
four people are ill. Suppose that the illness stays in Manhattan, 
which has about 1.6 million people. Also assume that it would take 
10 days to create a vaccine. 

• On the first day, one in 1.6 million people is ill. Even after a week, 
only 64 people are ill. After two weeks, about 8,000 people have 
been ill, or half a percent of the population. After three weeks, or 21 
days, a million people are ill, and the next day, everyone is infected. 

• Note that on day 20, you only have 30 percent ill. This means that 
if you wanted the vaccine available on day 20, when 32 percent 
of the population is infected, then you needed to start it on day 
10, when 512 people, only 0.032 percent of the population, are 
infected. That’s difficult to see. Exponential growth can, to a certain 
extent, appear to be doing almost nothing and then spike. That’s 
why paying attention to small changes can be quite important. 

• Having a bigger, faster computer isn’t enough. If you have an 
inefficient algorithm, it may take thousands of years to solve 
certain styles of problems. They are simply that difficult. No quick 
algorithm is known, in fact, some problems can be shown to be 
computationally intensive. The encryption problem for credit cards 
is this type of problem. 


• Searching is one big thing to do with data. Another is sorting. One 
way to sort is to put everything in a pile and take the items one by 
one and insert them into their proper place. It works. However, for 
large data sets, that can be slow. 

• There is another technique that is faster, which usually means it’s 
a bit more complicated. This method uses the idea of dividing and 
conquering. The idea is to split the array into two lists. One list 
contains items less than some value, and the other list contains items 
greater than or equal to some value. Sort both lists and recombine, 
which is easy. This is called Quicksort, because it is quick. 

• Suppose that our list is 5, 3, 7, 4, 6. So, we pick the value that will 
determine what goes into the two piles. We call that the pivot. Let’s 
pick 5, because it is the first element in our list. Now, 3 goes in the 
first pile, because it is less than 5, as does 4. So, 5, 7, and 6 go in 
the other pile. 

• So, we sort the first pile into 3 and 4. We sort the other pile into 5, 
6, and 7. Note that when you combine, the left pile is sorted, and all 
elements are less than those in the right pile. Combining is trivial. 

• If your list is bigger, then you simply apply the same idea to the 
two piles you create at the first step. You again divide them into 
two pieces. This is called recursion. Keep using the idea to produce 
smaller versions of the same problem until the problem is small 
enough that it is easy to do. When you get down to 10 items, for 
example, it is quick and easy to sort. Then, backtrack up the ladder 
of recursion and combine those lists 

• Try this the next time you need to sort through a pile. Computers 
make this much faster, of course, but it’s the algorithm that’s the 
key. The algorithm is what makes it easier to manage complexity, 
whether it’s slips of paper or big data in a computer. 


Lecture 5: Algorithms—Managing Complexity 

• Keep in mind that sometimes we can work with small data sets and 
gain great insight. However, we also want to keep in mind what 
can change when we work on large data sets, especially the even 
larger-sized data that might follow—or else what we do today may 
become obsolete tomorrow. And beware of thinking that bigger 
computers alone make things quicker. 

• Imagine if Google’s search algorithm didn’t scale. The number of 
web pages has grown at an alarming rate—a genuinely exponential 
rate. If they could not find or develop scalable algorithms, then 
Google may have been great with millions of web pages but failed 
to keep up with billions of web pages—or failed to keep up today, 
when the number of web pages is estimated in the trillions. By 
scaling up by 100 or a million in size, the time needed could have 
suddenly become problematic. 

• Imagine if, a year from now, given the growth in the size of the web, 
it suddenly didn’t take a second or less to get a search result from 
Google. Now, it took an hour, or overnight. Google as we know 
it wouldn’t be Google. Part of Google’s success was its ability to 
adapt its algorithms to the growing size and complexity of the web. 

• Today’s developers of applications and services must think ahead. 
There may be 10,000 users now, but if things go well in today’s 
age, there could be several million users. Can the current methods 
deal with huge, even nonlinear, increases in scale? Sometimes, 
faster algorithms have a more complex design, with some details 
that enable increased speed. More complexity in the algorithm can 
mean simplifying what your computer needs to do to finish the job. 

Suggested Reading 

Levitin and Levitin, Algorithmic Puzzles. 
Johnson, Simply Complexity. 



1. Search Google news for “exponentially,” and look for examples where 
the growth is most certainly not exponential but instead just fast. 

2. You may wish to watch the movie or read the book Pay It Forward. 
The thesis of the book is how quickly an idea can spread. By 
expecting a doubling in an event, we see an example of the impact of 
exponential growth. 


Lecture 6: The Cycle of Data Management 

The Cycle of Data Management 

Lecture 6 

S toring information is something that data analysts have been doing 
electronically for decades. But today, we are also dealing with huge 
data sets, and in order to analyze them, we have to have a better 
understanding of how they can be stored, and then how data can be retrieved. 
There are a variety of approaches, but some of the questions that need to be 
addressed are as follows: What data do you need to collect? How much of it 
do you need? What happens if you run out of storage? These issues are ever¬ 
present with today’s large and growing data sets. 

Data Storage 

• In data analysis, data is often 
stored in digital rather than 
physical form. Digital storage and 
reference isn’t new. Computers 
can search large data sets, like the 
155.3 million items in the Library 
of Congress, very rapidly. And 
we are now accustomed to such 
speed. These days, a second may 
be too long to wait for results. 
Google engineers have found that 
if search results are just a tiny bit 
slower, even by the blink of an 
eye, people search less. 

Finding enough storage space 
for large amounts of data can 
be a challenge. 

• There are multiple search engines. 

And in the cyberland of searching, 

it is definitely survival of the fittest. The fastest to retrieve what you 
queried and return results is, indeed, the fittest. Research showed 
that we will visit a web site less often if it is slower than a close 
competitor by more than a quarter of a second (250 milliseconds)— 
it takes 4/10 of a second (400 milliseconds) just to blink your eye! 


Kim Steele/Photodisc/Thinkstock. 

• The Library of Congress is archiving all of America’s tweets. This 
isn’t all the content of Twitter, but it’s still a lot of information, and 
depending on the moment, it can be a whole lot. The volume of 
tweets isn’t uniform from moment to moment, but Twitter typically 
gets 500 million tweets per day, for an average of about 5,700 
tweets per second. 

• When you hear that an organization like the Library of Congress 
is archiving something like tweets, think about the amount of data 
they are storing. Also think about how fast the act of storage has 
to be during a spike. Then, think about the further challenges of 
making the archive accessible. Users will want it to be stable, 
fast, and convenient. Then, multiply the hundreds or thousands of 
terabytes just from Twitter across a lot of other organizations: There 
are many, many data sets today that are very, very large. 

The Cycle of Data Management 

• For a data set to be meaningful and useful, it must first be stored 
and second be accessible. With such amazing volumes of data, 
how can this be done? In a nutshell, not with yesterday’s methods. 
Some of the storage techniques that worked with data sets that were 
considered large a decade ago are now dated. 

• There can be different approaches used to handle large data sets, 
especially very large data sets. However, size alone isn’t the only 
determining factor in how to approach managing data. Another 
issue is whether the data is in motion or at rest. 

• Using the example of Twitter, data at rest would consist of analyzing 
past tweets about a product to get a sense of customer satisfaction. 
Data in motion could have the same goal, but the difference is 
immediacy. In this case, the company might keep track of tweets as 
they appear. When a product is rolled out, what are people saying? 
This could help, for example, with quickly changing some aspect 
of the promotion, or customer service, or even the product, if 
that’s possible. 


Lecture 6: The Cycle of Data Management 

• There is also a cycle to data management. Of course, you must 
collect data. But what data you collect depends heavily on what 
problem or question you are considering. The cycle of data 
management involves the following questions: First, does the 
information even exist? Is there data to reach any conclusions? And 
then, if it is there, how do we capture it and know what it is stating? 

• Once we have data collected, we must decide how to store it and 
how to organize it. We may also need to bring together data from 
multiple sources—this is called data integration. And if your data is 
in motion, it may be better to have data integration happening over 
and over. You may even want data integration in real time, which 
is the opposite of dumping everything just once into a static and 
fixed database. 

• An important aspect of data integration, even if you do not have 
data coming from disparate sources, is preparing your data for 
analysis. After the data is integrated, we can finally analyze it. For 
example, Amazon will look at past customer actions from all the 
data that’s been integrated in its system. Then, they can make an 
action—for Amazon, that may be recommending a book. 

• But the cycle of data management goes back up to capture. Once 
we act, we need to capture more data and validate the action. For 
Amazon, if they keep recommending books that aren’t of interest, 
then the feature could even become a nuisance. 

Data Warehouses 

• When we begin a project in data analysis, we are fundamentally 
looking at how data will be managed. First, how much data will 
you have? A second important issue is the security of the data. How 
secure must it be? Third, how precise should the data be? Can we 
aggregate information? 


• Let’s assume that you are, in fact, using quite a bit of data—enough 
that you can’t easily store it on your own computer or even with one 
external hard drive. At one time, you’d need a supercomputer to aid 
in this. For most of us, that means that any such questions simply 
couldn’t be considered. But today, that’s changed. The change came 
when companies like Yahoo, Google, and Facebook turned their 
attention to helping offer storage mechanisms for the data that their 
services were helping produce. 

• An important part of storing and accessing data is data warehousing, 
which serves as a central repository of data collected and then 
integrated from one or more disparate sources. Data warehouses 
often contain both current and historical data, enabling one to look 
at current patterns and compare them to past behavior. This is an 
important feature; data warehouses generally are used to guide 
management decisions. 

• A data warehouse is a relational database that is designed for 
query and analysis rather than for transaction processing. As 
mentioned, a data warehouse often has multiple sources feeding 
into it. For example, there may be multiple branches of a bank 
in several countries with millions of customers and various lines 
of business from savings to loans. Each bank’s database may 
have been developed or tweaked internally, with each application 
designer making individual decisions as to how an application and 
its associated database should be built. As mentioned, these sources 
are combined in a data warehouse. A smaller subset of the data 
warehouse, aimed at end users, might be called a “data mart.” 

• The key, though, is that the data sources are also accessible. That’s 
the “relational database” part. A relational database is a type of 
database that organizes data into tables and links them based on 
defined relationships. These relationships enable you to retrieve and 
combine data from one or more tables with a single query. 


Lecture 6: The Cycle of Data Management 

• Parallel processing is one of the cornerstones behind modern-day 
large-scale data analysis. In particular, Apache Hadoop is a free 
programming framework that supports the processing of large data 
sets over multiple computers. Interestingly, the word “Hadoop” is 
named after the creator Doug Cutting’s child’s toy elephant. But 
the technology was initially sponsored by Google so that they could 
usefully index all the rich textural and structural information they 
were collecting. 

• However, the key is that they didn’t want to just store it—they 
wanted to present meaningful and actionable results to users. 
Nothing like that existed, so they created it themselves. This isn’t a 
project of Google alone. Yahoo has played a key role in developing 
Hadoop for enterprise applications. 

• What is characteristic of modern big data storage issues is that new 
techniques need to be created, so they are. Ironically, we aren’t 
always aware they happened, because sometimes they are created 
so that we never see their impact. The data world doesn’t feel any 
bigger to the user. We can keep posting updates on Facebook, for 
example, and Facebook is in a new big data center, ready to capture, 
analyze, and present it until they inevitably need to move again. 

• The issue of data storage is one that comes up more and more in data 
analysis. If your business is especially dependent on analyzing huge 
amounts of web data, then Hadoop can be part of the solution. But 
you don’t need any of that to tap the power of relational databases, 
which are your first step up when a spreadsheet is no longer big 
enough to hold your data. And if you have only megabytes or even 
gigabytes of data, you are unlikely to gain either speed or flexibility 
from putting your data into a Hadoop rack of computers. 

Suggested Reading 

deRoos, Hadoop for Dummies. 

Hurwitz, Halper, and Kaufman, Big Data for Dummies. 



1. Find a data set related to your interests and create a relational database. 
Note that we worked with a rather small data set in the lecture. You 
may wish to try a small and a large set of data and see how it organizes 
the data. 

2. To gain an appreciation for data warehouses and Hadoop, conduct an 
Internet search and see the array of topics, news articles, and companies 
on this topic. 


Lecture 7: Getting Graphic and Seeing the Data 

Getting Graphic and Seeing the Data 

Lecture 7 

G raphics are a terrific first step to really getting a grip on your data. 
But it’s only recently that graphical tools have become easy to 
create. In the past, graphics were used only at the end of an analysis, 
to summarize what’s been done—and sometimes as a tool of persuasion. 
When done well, graphics tell a story. They tell a limited story, but they can 
tell it clearly. And seeing the story can offer new insights. 

Graphics as Storytellers 

• In modern times, we have many tools that make it very easy 
to create graphics. This has led to a realization that graphics can 
be used throughout an analysis, not just to present final results. 
In 1977, John Tukey published a highly influential book entitled 
Exploratory Data Analysis. He recommended graphing your data 
when you start. He especially recommended graphing to see five 
things: the two extreme values, the median value (the one in the 
middle), and the two quartile values (cutoff points where 25 percent 
of the values are above or below). 

• The general process of data visualization starts with your data. 
Then, you often need to transform the data—for example, by 
looking at it in a few different ways. Then, you visualize the data. 
With that, you can analyze and interpret what you see. Keep in 
mind that sometimes this leads to wanting to look at the data in a 
new way. Then, you visualize again and analyze your new results. 
In this way, you create a cycle that allows you to gain more insight. 

• In his 1993 book Visualizing Data, William S. Cleveland of Bell 
Labs declared that “visualization ... provides a front line of attack, 
revealing intricate structure in data that cannot be absorbed in any 
other way.” Data visualization can help explore data, but it only 
begins the process. A picture is worth a thousand words, but no one 


picture conveys all the data equally. Graphics, like good writing, 
tell a story. And, like any storyteller, graphics tell the tale from their 
point of view. 

• As an example, a pie chart yields less information than some other 
graphs. We can use a pie chart to highlight the top few values, but 
it tells us less about the rest of the data. So, an important part of 
any graphical process is deciding what design to use. It isn’t always 
obvious what you need to see. You may not even know that some 
insight resides in the data. But graphical displays can help you 
efficiently and quickly see information. 

Look carefully at today’s information graphics, and you’ll often see 
common graphs like bar charts or pie charts. They are artistically 
done. But it’s the same basic graphical techniques. In fact, we’ve 
probably all been stuck looking at a very common bar chart used to 
give the status of a download 
or update. It doesn’t always 
work accurately, but when it 
does, it gives some potentially 
useful information. 

Why do graphics help convey 
information? Why can they, 
when done right, tell a story 
so quickly? Part of this comes 
from the way our brains 
think. Pictures, unlike text, 
are processed all at once. 

Approximately 30 percent 
of our total gray matter in 
the brain is responsible for 
visual activity. That’s why we can communicate with pictures so 
effectively. We need only to look at stories from 35,000 years ago 
as told with drawings on rocks and walls to see that pictures have 
been working well for a very long time. 


Data visualization is an important 
part of data analysis. 



Lecture 7: Getting Graphic and Seeing the Data 

• On the other hand, the brain processes text linearly as it moves 
through letters or words. That takes longer, and most readers skip 
a lot. A web usability expert named Jakob Nielsen found in a small 
study that the average person will read only about 20 percent to 28 
percent of the words on a web page. So, in this era, visual content 
that communicates quickly and effectively can tell a story that 
words cannot—at least if someone never reads those words. This 
helps explain why many of today’s magazines and online resources 
use graphics and infographics to tell their stories. 

• For example, USA TODAY has their daily snapshot. In 1982, the 
paper departed from text-centric, black-and-white-newspaper 
format and moved to color and graphics to tell part of the story. 
The British Sunday Times and Time magazine have used graphics 
to simplify and enhance the understanding of their stories. The New 
York Times has invested more effort in producing sophisticated 
graphics, many of which are interactive. 

Making a Good Graphic 

• A potential pitfall of infographics is that it’s possible for a graphic 
to omit the important conclusion you are trying to find. Data can 
contain information that can be perceived quickly with a graph, but 
we must choose the right graph in the right way, or it’s possible that 
the important information might be concealed. 

• Ideally, graphics portray data in a quickly consumable and easily 
understood fashion. This can be, and is often, done. You can see 
this in Leonardo da Vinci’s Vitruvian Man from the late 1400s. This 
classic image graphically shows how to understand the proportions 
of the human body as written by the Roman writer Vitruvius 15 
centuries earlier. 

• For example, the head as measured from the forehead to the chin is 
one-tenth of the total height. One’s outstretched arms are always as 
wide as the body is tall. Now, this may not be true; Vitruvius might 


be wrong. Blit until Leonardo da Vinci, no one cared much about 
the data analysis presented by Vitruvius. It’s the graphic that tells 
the story in a compelling way. 

• What goes into making a good graphic? Two questions lie at the 
heart of a graphic. Who is the graphic for? What does it need to 
communicate? Again, think of a graphic as a story. For example, 
you’ 11 tell a story of a company differently to stakeholders than to 
customers—they have different needs. What story are you telling? 

• Edward Tufte’s 1983 book entitled The Visual Display of 
Quantitative Information is a definitive resource on visually 
displaying data for many statisticians. In his book, he notes, 
“Graphics reveal data. Indeed graphics can be more precise 
and revealing than conventional statistical computations.” But 
we also want to be aware that even a great graphic does not 
include everything. 

• So, there is another important key in data visualization: What data 
do you visualize? This can be both an art and a science. It’s not 
enough to know the general area for an archeologist to dig to find 
ruins. While there may be hundreds of miles to consider, being half 
a mile off can still lead to empty results. Visualizing the right data 
can led to discoveries and new insights. 

Graphing in Four Dimensions 

• Let’s step beyond the third dimension and graph data in more than 
3-D. First, let’s see how we can graph three dimensions in 2-D. We 
see this all the time on weather maps. Think about how weather 
maps start with 2-D spatial data and temperature data on top, where 
the temperatures are represented by color. Even 4-D is possible on 
a flat map, and that fourth dimension doesn’t have to be spatial. 
Think of those weather maps. The third dimension is color. A fourth 
dimension could be humidity, or precipitation, or storms. 


Lecture 7: Getting Graphic and Seeing the Data 

• Circular histograms, or rose diagrams, are another way to put 
several dimensions on a single graph, with each wedge representing 
one dimension. Star plots are a cruder version of the same idea, 
where the result looks like a star instead of a flower. 

• One of the more interesting ideas for graphing more than three 
dimensions is to plot data using Chernoff faces. Herman Chernoff 
invented this graphical technique to display data in the shape of 
a human face. A face has eyes, ears, a mouth, and a nose—that’s 
four dimensions. It’s almost like a Mr. 

Potato Head for data visualization, except 
the data itself determines what we see. 

And it’s not just the size of the features; 
it can also be the shape, placement, and 
orientation of the facial features. 

• Humans easily recognize faces and notice 
even small changes without difficulty. 

Chernoff faces allow us to pack a lot 
of dimensions on a small graphic that’s 
easy to compare. This type of graphic 
encompasses a lot of information, and 
it means more if you already know and care about the underlying 
data. But even with this type of graphic, as always, the graphic 
cannot portray everything. 

• Another issue is how the graphic itself can push your story in a 
particular direction. For example, with Chernoff faces, we perceive 
some features more than others. We notice eye size and eyebrow 
slant a lot, so they tend to carry more weight. 

• You need to be careful: How you map the variables to the graphic 
can affect what people who view the graphic notice. In general, you 
should think carefully about which features of a graphic will attract 
the most attention. Such limitations are inherent in any graphic 
and simply should be kept in mind when presenting or interpreting 
graphic results. 

Chernoff faces allow 
people to display 
data in the shape of a 
human face. 


© The Teaching Company. 

Suggested Reading 

Smiciklas, The Power of Infographics. 

Tufte, The Visual Display of Quantitative Information. 


1. The New York Times is a leader in interactive graphics. Look at their site 
and find your favorites. You may also want to look at the site 
for examples of such graphics and, if you like programming, download 
templates for your own use and exploration. 

2. Create your own infographic. You can create a single infographic of a 
piece of data or create a poster, which often works best in three sections 
with some overarching theme. You may also want to look online for 
tools that help. There are a variety of free tools to aid. 

3. Search Google for “infographic” and enjoy the image gallery of options 
and creative ideas. Be critical in your viewing. Does the graphic tell its 
story effectively? 


Lecture 8: Preparing Data Is Training for Success 

Preparing Data Is Training for Success 

Lecture 8 

A ll data is not created equal. There can be errors and ambiguities in 
data, and we must determine what to include or exclude. We need 
what is called data preparation. In addition, all data does not have— 
or should not have—the same purpose. That’s because we often need to 
peel off part of the data to help us figure out how predictive or effective our 
analysis is. We divide our information into training data, which we use first 
to build our analysis, and test data, which we use afterward to check. 

Training Data and Test Data 

• The set of data that is the data we develop our idea on is called 
the training set. We also need to test our idea, and it often doesn’t 
work to use the set we already have. So, we turn to another set of 
data. This is called the test set. The data we are using is separated in 
order to be used this way. It is very possible to design a method that 
does great on the training set but fails on the test set. 

• On one level, it sounds simple enough to have the two sets of 
data. But it’s not always that simple. The issue of really testing an 
idea can be quite subtle, and it can become troublesome when not 
done correctly. 

• In fact, a poor training set can doom data analytics. There are a 
number of issues to consider. First, the training set must cover the 
full range of values that the problem might present. Suppose that 
you are creating a method to predict housing prices. You’d want 
expensive and inexpensive houses, big and small houses, one- and 
two-story houses, and houses with and without garages. The more 
features that exist, the larger your training set should be. There 
isn’t, though, an easy way to know how large the set should be. 
You do want dozens, if not hundreds or thousands, of examples 
of each feature. 


• An issue with training and test sets is that they often come from the 
same set of data. You have access to only one set of data. From it, 
you want to create a training and a test set. Ideally, the training set 
would be representative of the whole set. In the same way, without 
being identical, the test set would also be representative. However, 
you probably don’t know in advance exactly what representative 
means—especially when dealing with really large data sets. 

• So, how do we do this? Let’s assume that we have our data in a table. 
Each row represents a person’s ratings of movies. We can create the 
training set by selecting random rows—about 80 percent—of the 
data set. The remaining rows are the test set. We probably don’t 
know if this particular test set works, but the rows of data were 
created at random, so we can simply do it again. In this way, we can 
create a few training and test sets and see how we do. This tests the 
robustness of our method. As long as we keep any given training set 
entirely distinct from its corresponding test set, we’re okay. 

• This assumes that your data is ordered randomly, or at least placed 
randomly into training and test sets. If that’s not the case—if the 
training data differs from test data in some way—then this approach 
might not work. Clearly, there is subtlety here, and what to do in 
those cases is still an area of continued research. 

• In many ways, this shouldn’t be all that surprising. For example, we 
have been trying to create representative samples in political polling 
for years. We have, at various times, created successful ways to do 
this. However, when the technological landscape changes—or the 
issues change, or the nature of the population changes—we need 
new ways to specify our training and test data, too. Note that putting 
data into training and test sets is done prior to creating methods that 
analyze them. If the goal is to predict some phenomenon, then we 
process the data in this way prior to creating our method. 


Lecture 8: Preparing Data Is Training for Success 

Dirty Data 

• Data can be what is called dirty. If data is dirty, how can you trust 
your results? Data can be incomplete, noisy, and inconsistent. This 
can be due to human error, limitations of measuring devices, or 
flaws in how the data was collected. It can be that nothing really 
went wrong but the data isn’t in necessarily the assumed form. For 
example, one person could have two entries in the data that the 
U.S. Postal Service is using due to changing addresses. It could be 
important not to double-count the individual. 

• However, if we end up with data that isn’t descriptive, it can be 
essentially garbage. If this is the case, how can you expect to make 
meaningful insights? This leads to the saying “garbage in, garbage 
out.” This is a common expression in computing. 

• So, what does garbage look like? How dirty is it? Well, it may not 
look all that dirty to us. Consider this simple question. Are Bobby 
Miller from Charlotte, North Carolina, and Bob Miller also from 
Charlotte, North Carolina, the same person? We might guess that 
it’s the same person. But a computer would treat this as two distinct 
people. These issues can be very real. 

• Conversely, there can be two addresses for one person. Mail can 
come to a house not addressed to the very small town that it is 
actually located in, but to a neighboring larger town. As such, the 
house might be listed in some databases as being located in the 
neighboring larger town. At some point, it would be easy for such 
a data set to list the house as being located in two, or even three, 
different towns. 

• A computer, without help from specialized software, could easily 
see such entries as being two, or more, separate people. This may 
not be a big deal by itself, but when merging corrupt or erroneous 
data into multiple databases, the problem may be multiplied by 
millions. What’s the point in having a comprehensive database if 
that database is filled with errors and disputed information? 


• One way to deal with this problem is to buy software to clean up the 
data. This isn’t all that easy, as can be reflected in the price of such 
software. A general tool powerful enough to address your particular 
issues can cost between $20,000 and $300,000. What is it fixing? 
What could be going wrong? One issue is duplicate data, such as 
the house being listed twice. A variety of issues can occur, and how 
to deal with them isn’t always all that obvious. To ensure that this 
doesn’t happen, data analysts generally must look at the data to 
ensure that it makes sense. 

• To see how easily variation from differing data sources can 
introduce small, but serious, inconsistencies, consider a few cases. 
Were postal codes recorded using a uniform format? In the United 
States, that translates to the following: Do the zip codes all use the 
same five-digit format? If addresses are encoded with something 
like bar codes, does the code have enough flexibility to describe 
every type of address? 

• Are dates all the same? If you have data coming from the United 
States and from Europe, they likely are not. This is because in the 
United States, we write dates in day-month-year format. However, 
in Europe, dates are often written in year-month-day format. A date 
like 10/11/12 can cause a lot of confusion. 

• Is data in the same units? If you are looking at currency, is the 
data global—and if so, are the countries reporting in their native 
currency or some common currency? Is everyone reporting values 
with the same accuracy? 

• Is some data missing? This is not unusual. The data might not have 
been collected. Maybe some people declined to report their age or 
weight. It may be that some data isn’t applicable. For example, not 
everyone has a middle name. 

• Regardless of why the data is missing, what will you do when it 
isn’t present? There are several options. First, you can eliminate 
data objects that have missing elements. But then you are throwing 


Lecture 8: Preparing Data Is Training for Success 

out data, and if the data is missing from an important subset, you 
might be removing most or all of that set. You could also delete 
an attribute that has missing values. For example, maybe you no 
longer include middle names or ages. Again, this removes data and 
should be done with caution. 

• Sometimes, you can estimate a value; sometimes, you can simply 
ignore that a value is missing. This depends on the analysis and the 
type of method being derived. 

• Another common attribute is having inconsistent values. Sometimes, 
this is easy to check. For example, heights can’t be negative, and 
adult ages shouldn’t be single digits. Other times, possible mistakes 
are not easy to check but can still affect the results. 

Making Data Clean 

• To aid in dealing with these issues, data is often preprocessed, or 
made clean—or at least clean enough. Data preprocessing comes in 
two main forms: 1) selecting data objects and attributes for analysis 
and 2) creating or combining attributes to create new attributes more 
suitable for analysis. In selecting data objects, you might sample 
the population or look at only a subset of the available features of 
your data. Other times, you might decide that you need to add an 
attribute—some data you hadn’t collected before. Other times, you 
might combine data. 

• In his 2012 book entitled Best Practices in Data Cleaning, Jason 
W. Osborne stated that recent surveys of top research journals in the 
social sciences reveal that many academic authors are not suitably 
concerned about dirty data. 

• The main problem is that most statistical tests may not be robust 
or reliable with dirty data—at least to the degree that researchers 
might hope. But even if you’ve done everything right initially, 
similar problems can pop up later. Things can quickly and 


almost unexpectedly go astray. Many issues can occur that cause 
inconsistencies, changes, missing values, or other problems in data, 
creating results that are truly garbage. 

• The other problem is that we have to create results that work on 
good, clean data. But our goal is generally not to simply do well 
on existing data—that can lead to a problem called overfitting. We 
want to create a method that can use that data to perform well, but 
also offer insight into data that is yet to come. 

Suggested Reading 

Osborne, Best Practices in Data Cleaning. 

Tan, Steinbach, and Kumar, Introduction to Data Mining. 


1. Sometimes, rather than splitting your data, you can test your data on 
future events. This is also a way to train and test your analysis. Think of 
questions that have interested you in this series. Would you need to split 
the data, or could you test your analysis on future events? 

2 . Keep in mind that just because you test your data, it may not always 
work. First, random events can happen, and even more behavior can 
change. So, even a well-designed model may, in time, no longer work. 
As such, you sometimes will want to test again—even on analysis that 
is worldng well. 


Lecture 9: How New Statistics Transform Sports 

How New Statistics Transform Sports 

Lecture 9 

D ata plays a sometimes fondly remembered role in sports. Numbers 
shape how we watch sporting events. Numbers affect how players 
approach the competition. And numbers are important to how 
athletic performance is understood and assessed by everyone. In modern 
times, data analytics is playing a huge—and growing—role in sports. In this 
lecture, you will gain insight into why it’s growing, and you will learn how 
to do some sports analytics. 

The Pythagorean Expectation and Runs Created 

• Keeping track of data has long been part of many sporting events. 
Sports data can offer insight that is otherwise difficult, if not 
impossible, to perceive. Baseball box scores, for example, not only 
record which team is winning and losing as a game progresses, 
but they also include summary information about the performance 
of every player in the game. Beyond individual games, there 
are statistics summarizing the data for entire seasons—and for 
entire careers. 

• Statistics and box scores can unveil various aspects of the game. 
But they simplify at that very same time. In some cases, a particular 
statistic isn’t very instructive. In other cases, it is a matter of having 
that statistic plus several other statistics. 

• But under every summary statistic is the need for data, and it is the 
data that we are analyzing. Today, data collection and analysis is an 
art and science of its own. In modern times, we can look at all of the 
data—not just statistics that summarize the data. 

• Let’s consider the math behind the 2011 film Moneyball and the 
2003 book by the same name. First, let’s set the stage for the role of 
analytics in the story. The drama comes from the tension between 
two factors: winning and money. 


• Billy Beane became the general manager of the Oakland Athletics 
baseball team in 1997. But he had one of the smallest budgets in 
Major League Baseball. Other teams were still building their rosters 
with the conventional wisdom of the time. They used their allotted 
budgets to sign big-name 
hitters and rocket-armed 
pitchers. Even the A’s had 
followed that strategy in the 
1980s, winning three 
consecutive World Series 
appearances in 1988 
through 1990. But in the 
1990s, new ownership 
wanted to spend less on the 
team, so the cash-strapped 
A’s needed a new strategy. 

• Beane and his staff decided 
to try something different. 

They built their team 
by being really good at 
data analysis. The most 
expensive players were 
overvalued at the time, 
and even the wealthiest 
teams didn’t have unlimited budgets to spend. So, Beane’s 
strategy was to buy players that had less value as measured by the 
traditional techniques, which often involved intuition, foresight, 
and experience. 

• Beane’s method worked. In 2002, his team became the first in over 
100 years of American League baseball to win 20 consecutive 
games. They also made the playoffs, reserved for the top eight 
teams in baseball. This all came while having the smallest player 
payroll of any Major League Baseball team. So, how did they 
cobble together such a team for a fraction of the budget available to 
many other teams? 


Billy Beane’s method of data analysis 
led the Oakland A’s to victory while 
cutting costs. 


© Ezra Shaw/Getty Images Sport/Thinkstock. 

Lecture 9: How New Statistics Transform Sports 

• In the film, Billy Beane’s assistant, the character of Peter Brand, 
states that the team needs to win at least 99 games to guarantee a 
playoff spot. What type of team can win 99 games? The statistic 
we’ll learn now was developed by Bill James, a baseball writer and 
statistician who is connected to many of the techniques detailed in 
Moneyball. What James discovered was a statistic combining the 
total number of runs a team scores and the total number it allows. 

• Here’s how it works. A fraction will estimate the percentage of 
games a team will win. The numerator equals the square of the 
total number of runs scored by the team that season. So, if a team 
scored 100 runs, for example, the numerator would be 100 squared. 
To get the denominator, we just add the numerator (the square 
of the number of runs scored by the team) to the square of the 
number of runs allowed by the team. This formula is known as the 
Pythagorean expectation. 

• As an example, let’s look at the 2002 Oakland A’s. The team scored 
800 runs and allowed 654 runs during the regular season. 

80Q 2 

So, the Pythagorean expectation equals -. 

800 2 +654 2 

Put this into a calculator and you' 11 find that the A’s were expected 
to win 59.94 percent of their games. The team played 162 games in 
the regular season, and 59.94 percent of 162 is 97.1. 

• There are some interesting aspects of data analysis here. First, the 
Pythagorean expectation didn’t quite reach the 99-win threshold. 
They won 103 games—not 97.1—and they made the playoffs. The 
number we calculate is a calculated guess; it’s not a crystal ball. 

• It can be easy, as a data analyst, to get attached to your 
computations. But remember that they give insight—an opinion. 
In the case of the A’s, they won slightly more games than 
this Pythagorean expectation calculates. So, the formula wasn’t 


perfect. Maybe it could be refined. The important point is that just 
by using the Pythagorean expectation, the A’s got much closer to 
identifying their own winning formula. 

• Ok, so now we know that the Pythagorean expectation uncovers a 
distinctive relationship between wins and runs scored and allowed 
during a season. This is something a coach and the players can 
concentrate on. But how? We can use more data on the players 
themselves to think about how to reach our target of 99 wins. 

• In the film. Brand indicates that the team can allow no more than 
645 runs (which is 9 fewer than were allowed in the 2002 season). 
Taking this number as fixed, how many runs must be scored to 
reach an expected number of wins greater than or equal to 99? 

• Let’s put these pieces into the Pythagorean expectation formula. 

99 jr 2 
162 jc 2 +645 2 

• So, we get 

x 2 +645 2 

162x 2 


• Moving the x 2 to the same side, we get 

• So, 

x = 


• This means that the team must score more than 808 runs. 


Lecture 9: How New Statistics Transform Sports 

• So, we know how many runs we need to score, and we know the 
maximum number of runs we can allow. But how do we know 
whom to sign? For this, we turn to another Bill James statistic 
called runs created, which quantifies approximately how many runs 
a player contributes to the team. 

• Like the Pythagorean expectation, runs created is a fraction. The 
numerator equals (hits + walks) times total bases, where a single 
is worth 1 base, a double is worth 2, and so forth. The denominator 
equals the number of plate appearances by that player. 

• This is a great example of how we can use data analysis to come 
up with innovative solutions. In particular, note how we combined 
ideas to create workable insight. This is an important element 
of data analysis. You create tools, but you use many of them to 
create something. 

• This approach to baseball is now well known and used by many 
teams. But that doesn’t mean that all opportunities are gone. In 
later seasons, for example, the A’s began selecting players more for 
undervalued skills in defense. And they also began using data on 
players who had not even begun to play professionally. 

Advances in Sports Analytics 

• From building a team to recognizing improbable performances, 
math can give us insight when it comes to sports. What else can 
we analyze? That’s a question that many professional athletes 
and teams are answering. With modern technology, there are new 
answers to that question as new discoveries are made. Some are 
made public; others are kept very private, because they give a 
competitive advantage. So, look for new statistics being kept in a 
sport or new graphics appearing in the news to detail a performance 
in a sport. This often reflects advances in sports analytics. 

• A key of sports analytics is that it can help us know when something 
special is happening. Keeping box scores enables us to keep track 
of battering averages. It can help us recognize who to recruit for 


the A’s and make a play for the World Series—without spending 
the most money. It can also help ensure that a team on a losing 
streak simply may be on the unfortunate side of randomness. This 
can improve coaching decisions about a team or player. This can 
enable a recruiter to recognize greatness on the field. If we turn 
to predictive analytics, it might even indicate an attribute that 
correlates well with future play. 

• The key is knowing which analytics are useful and insightful. No 
statistic ever tells the whole story, but a good statistic can tell us 
which part of the story to look at more carefully. And for coaches 
and players, better use of data makes it possible to change the 
story—better data analytics is game-changing. 

Suggested Reading 

Baumer and Zimbalist, The Sabermetric Revolution. 
Lewis, Moneyball. 

Winston, Mathletics. 


1. Are you interested in sports analytics? Which sport? Look online for 
downloadable data sets. For example, the Olympics offers downloadable 
data at This isn’t always the easiest format. 
You may want to search for data sites. For example, the Guardian 
has data for the London 2012 games. You can view it at http://www. 

2 . Pay attention to the statistics and predictions made in sports programs. 
Many use new ideas to predict future events. You are hearing the new 
waves in sports analytics. 


Lecture 10: Political Polls—How Weighted Averaging Wins 

Political Polls—How Weighted Averaging Wins 

Lecture 10 

S tatisticians have been conducting election polls for many decades. 
But such polls aren’t always accurate, especially for close elections. 
But recent data analytics does much better than ordinary polling. 
The secret is combining multiple polls. It turns out that clever aggregation 
of multiple data sources produces much more accurate predictions. And 
that also transforms political campaigns. But data aggregation is not just 
for politics. Weighting and aggregating data can work well for any messy, 
complex field where no single variable explains everything. 

Predicting Elections Results 

• In the 1920s, political predictions were notoriously unreliable, and 
people were looking for new ideas on how to forecast the upcoming 
presidential election between Herbert Hoover and Alfred Smith. 

• A popular magazine of the time was Literary Digest, which 
employed a powerful new tool, called a poll, to accurately predict 
Hoover’s landslide victory in 1928. Their poll also accurately 
predicted Hoover’s demise in 1932. 

• Then came the next presidential election. The republican nominee 
in 1936 was Kansas governor Alf Landon, who was running against 
first-term president Franklin D. Roosevelt. The Literary Digest 
predicted that Landon would receive 57 percent of the popular 
vote to Roosevelt’s 43 percent. They came to this conclusion 
after sending out 10 million surveys and getting 2.4 million back. 
FDR, our future four-term president, won with 62 percent of the 
popular vote. 

• Why was the Digest so wrong in predicting this election, especially 
when it was so accurate in previous ones? This election occurred in 
1936, which put it deep in the shadow of the Depression. As a result, 
many people were rapidly eliminating luxuries from their lives. 


• The Literary Digest got their mailing list for their surveys from 
telephone directories, automobile registration records, and their 
list of subscribers. The key is that each one of these activities— 
whether we’re talking about using the telephone, driving a car, or 
subscribing to magazines—was considered an unnecessary luxury 
during the Depression. 

• This means that the Digest's method analyzed how the wealthy 
population of the United States would vote. But this isn’t what they 
thought they were predicting. They thought they had accurately 
predicted the election. 

• There was a young pollster of the time who recognized the errors in 
the Digest's data and made his own poll. He surveyed only 50,000 
voters, compared to the 10 million voters surveyed by the Digest, 
but he made his set of voters more representative of the voting 
population. And as a result, this new poll predicted FDR’s win with 
only half a percent of the data used by the Digest. 

• Maybe the most impressive part is that this new pollster saw the 
Digest’s error and was able to compute what their prediction would 
be within one percent. The new pollster was named George Gallup, 
and this is the beginning of the Gallup poll we see used everywhere 
today. George Gallup’s work in this election launched his career 
and gave the Gallup poll its initial credibility. 

• Almost 100 years later, in the 2008 and 2012 elections, Nate 
Silver emerged, in a way similar to George Gallup, as a new 
force in polling. Silver is a statistician and writer who initially 
made his name analyzing in-game baseball activity in the 
realm of sabermetrics. Then, he turned that statistical toolbox 
toward elections. 

• In the 2008 presidential election, Silver established his web site, At first, he didn’t reveal his identity. Soon 
after he did, he began to appear in various media outlets as an 
electoral and political analyst. In 2008, Silver correctly predicted 


Lecture 10: Political Polls—How Weighted Averaging Wins 

the winner of 49 of the 50 states in the presidential election. The 
only state he missed was Indiana, which went for Barack Obama by 
one percentage point. In that same election, he correctly predicted 
the winner of all 35 U.S. Senate races. It wasn’t difficult to predict 
some of the results; what was impressive was coming so close to 
giving correct predictions for all of the results. 

• On the morning of the 2012 presidential election, Silver posted the 
final update of his model, giving President Barack Obama a 90.9 
percent chance of winning a majority of the 538 electoral votes. By 
the end of the day, Mitt Romney conceded to Barack Obama. Silver 
also correctly predicted the winner of all 50 states and the District 
of Columbia. 

• Silver created a poll by combining the results from multiple 
pollsters. He was not alone in this approach. But it is important to 
note that individual pollsters were less successful. Rasmussen 

Predicting how people will vote in elections is a classic challenge that stumps 
even prediction specialists. 


Max Whittaker/Getty Images News/Thinkstock. 

Reports missed on six of its nine swing state polls. Gallup had 
among the worst results. In late October, their results consistently 
showed Mr. Romney ahead by about six percentage points among 
likely voters. This differed significantly from the average of 
other surveys. 

• How can so many specialists dedicated to the science of predicting 
elections vary and even struggle? First, just like Roosevelt’s second 
election, a major issue is polling a representative group. Gallup 
polls typically sample the opinions of 1,000 national adults with a 
margin of error of plus or minus four percentage points. If Gallup is 
creating a poll to gauge public opinion about a national issue, how 
can the opinion of 1,000 people represent the opinion of millions? 
That’s a key issue in the science of polling. 

• In polling, just like with the Literary Digest, you must poll a 
representative group. We must have a well-mixed sample of people. 
Not only should they be mixed, but in a way, the mix of the group 
should look the same as the large group. 

• An issue with this is that you must contact them. In the 2012 
election, some polls were done with live interviewers, other with 
automated telephone interviewers, and even others via the Internet. 
Of these three modes, automated polls had the largest average error 
of five points, having a Republican bias for that selection. 

• Another issue is whether you call cell phones. There are legal 
restrictions regarding automated calls to cell phones. As seen in the 
time of the 1936 Literary Digest poll, this can be tricky. How you 
contact poll respondents affects whom you reach. 

• For someone who wants to get started with making predictions, 
keep it simple: Just start with a single state, assign weights to each 
poll from that one state, come up with a prediction, and update your 
prediction as more polls come in. 


Lecture 10: Political Polls—How Weighted Averaging Wins 

• In an address at the Joint Statistical Meetings, which is the largest 
gathering of statisticians held in North America, Silver noted that 
the average is still the most useful statistical tool. It’s a cornerstone 
of all the methods that are making election predictions so much 
more reliable. 

Running Political Campaigns 

• Using data analysis, campaigns can better understand the electorate. 
In particular, they are able to better identify who they want to 
communicate with. In the 2012 election, the Obama campaign 
used data analytics to help know what demographic to reach for 
votes. Then, they could study what media markets tend to reach that 
group. They promoted their candidate there rather than buying time 
on media outlets that reached a large demographic. This approach 
was much cheaper and, in many ways, more valuable. 

• Political campaigns have always tried to identify and mobilize 
“their” voters. And campaigns have been amassing more 
sophisticated files on potential voters since the 1990s. Some experts 
credit this approach to the 1996 Bill Clinton campaign, which 
focused on winning swing votes rather than the entire electorate. 
George W. Bush narrowed the focus further by concentrating 
resources on swing voter Republicans. This makes sense, but to do 
this right, they needed to figure out who these people were, where 
they lived, and what they cared about. 

• Jump to the 2008 Obama campaign and its well-executed web 
campaign. They raised about half a billion dollars online. At the 
same time, they gathered a lot of data—around 13 million e-mail 
addresses and 5 million friends across social media platforms. 
E-mail addresses, combined with information from voter 
registration records, helped the campaign uncover which potential 
voters they should reach with rides to polling places, or which 
phone calls to make addressing specific points raised online. 


• Once campaigns have a digital profile of voters they’d like to 
target, they can also develop very customized political advertising. 
Campaigns can use online advertising techniques to be sure they 
present their message to their desired audience. 

• Online ads also offer quick feedback about how well they are 
working. Did you click the ad? How long did someone engage in 
pre-roll video before skipping it and getting to the featured video? 
In the last weeks of an election, this kind of rapid feedback can be 
especially valuable, but it also helps a campaign stay more on track 
throughout an election. 

• What makes the approach we’ve been discussing especially new 
and powerful is the effort to grab and use all of the relevant data 
about voters. There had been an entirely different approach to 
political prediction over the years that just ignored polling data. 
A model from that approach might have tried to predict voting by 
looking at something else: How is the economy doing? Is there a 
popular or unpopular war? The idea was that if you had a model 
explaining why voters will vote one way or the other, then maybe 
you don’t need polling data tracking how they say they will vote. 

• Data analytics has transformed national politics, in part, by taking 
the opposite approach. Instead of hoping for master variables to 
explain overall elections, get lots of data about voters. See how they 
vote, and see how they say they’ll vote. As long as there is plenty 
of voter data available to aggregate, this will very easily beat more 
speculative kinds of prediction. 

• This approach works well when there is a lot of data. The less data 
you have, the less confidence you can have in this kind of analysis. 
But the basic approach remains valid even for smaller elections. 
There’s just more room for error. 


Lecture 10: Political Polls—How Weighted Averaging Wins 

• If you want to know how people will vote, then focus on data about 
the voters. You may need to focus on other types of voter data, such 
as who they voted for previously, changing demographics of the 
district, and political contributions. Especially for small races, it 
may also become important to have very granular metrics about 
candidates themselves, especially how they interact with voters. 

Suggested Reading 

Bradburn, Sudman, and Wansink, Asking Questions. 
Silver, The Signal and the Noise. 


1. Search the Internet for political polls that might interest you. Can you 
find several? What elements can you derive that could help weight their 
importance, or do you believe that recency is enough? 

2 . Are you interested in presidential polls? Either looking back at the past 
election, or if one is coming, some claim that state polls are enough for 
aggregated polls, which may not be as complicated as Nate Silver’s 
work but can give insightful results. What data can you find? 


When Life Is (Almost) Linear—Regression 

Lecture 11 

A big part of data analysis is predicting future outcomes by studying 
the past. And we predict, where possible, by describing a perceived 
trend with a formula. The formula might be linear, exponential, 
parabolic, and so on, but whatever the formula, it’s rare to exactly match the 
data. Even so, a formula can be close to the data and capture enough of its 
essence to give insight on what might happen in the future. This lecture will 
teach you about the power of predicting data just by using equations of lines. 
In this sense, we are modeling life as if it were linear. 

Least Squares Regression 

• In 2012, Usain Bolt electrified the Olympic track and field stadium 
in London as he won a second consecutive gold medal in the 
100-meter dash. This was Bolt’s second time to win this title. In 
2008, Bolt ran the fastest 100-meter dash in the Olympics. No other 
Olympic gold medalist could have beaten him—until he ran even 
faster in 2012. 

• There have been 28 gold medalists in the 100-meter dash between 
1896 and 2012. The slowest time was Tom Burke’s 12-second 
sprint to gold in 1896. The fastest was Usain Bolt in 2012. In fact, 
in most of the Olympic Games, the gold medal time is faster than 
the previous Olympic winning time. 

• Bolt ran 100 meters in 9.69 in 2008 and 9.63 in 2012. It takes 400 
milliseconds, or 0.4 seconds, to blink an eye. So, Bolt beat his 
own time by less than the blink of an eye in 2012 versus 2008. 
Carl Lewis’s 1984 run took only 9.99 seconds; this was about a 
blink of an eye slower than Bolt’s 2012 race. Jesse Owens ran in 
10.3 seconds in 1936, which is almost 2 blinks of an eye slower 
than Bolt. 


Lecture 11: When Life Is (Almost) Linear—Regression 

• This gives a bit more insight on the numbers, but how far ahead 
would one runner be than another? To help with this, we’ll compute 
average speed. Bolt ran the 100-meter race in 9.63 seconds, which 
averages to a speed of 23.23 miles per hour. Carl Lewis averaged 
22.39 miles per hour. So, at the end of the race, Carl would have 
been 3.6 meters, or just under 12 feet, behind Bolt. Jesse Owens 
would have been 6.5 meters, or about 21 feet, behind. 

• Having a sense of the speed that these numbers represent helps 
us see how a slight variation can change the time, but very small 
changes are important in races at these speeds. Next, let’s get 
a sense of the numbers as a whole. The numbers are generally 
decreasing. One quick way that data analysts get a sense of data is 
to graph or visualize it. 

• There is some variability, but the data can be approximated by a 
line. While the line won’t pass through every point, it can get close. 
If you wanted to predict future times in this race, the insight you’d 
gain simply from graphing would be huge. You can model the data 
with a line, and if the trend in the data continues in the future, you 
can make predictions at how fast people might be running in 2040, 
for example. 

Olympic Running Times 








1880 1900 1920 1940 1960 1980 2000 2020 



• When trying to fit a line to the data, your tinkering could bring your 
biases into the computations. So, it is better to use all the data and 
let the data, without any fiddling on your part, create the line. If the 
points don’t lie on a line, we use the tool of regression, where we 
“regress” the data back toward whatever model we create. 

• Before producing the line, let’s think about what line to choose. We 
want to find a line that approximates the data. We want the line to 
be as close to all the points as possible, but the line may not pass 
through any of the points. We’ll find how far off the points are by 
their vertical distance to the line. 

• We could just measure the distances from the line, add those up, and 
be done. But that ignores the fact that some values are going to be 
close to any line we choose, while others will be farther away. We’d 
like to choose a line that minimizes those maximum distance points— 
keeps those far distances as small as possible. So, instead of taking 
the least distance for each point, we take the least squares distance. 
This is clever because by squaring, our biggest values are now huge, 
and minimizing those huge values gets much more attention. So, the 
least squares method takes the square of all those distances and adds 
those up. Whatever line makes that sum smallest is our best fit. 

• Using a least squares regression line for our Olympic times, we find 
the line y = -0.0133x + 36.31. There are a variety of tools—from 
Excel, to JMP, to SASS, to R, and many others—that can help you 
find the equation of the line. Using Excel, you simply have a table 
of data: one column containing the years and another containing 
the times. Then, you essentially hit a button and the formula comes 
out. Regression is a powerful technique that is quickly calculated 
on a computer. 

• The slope of the least squares line is -0.0133 (x is the year, and 
y is the time). The slope predicts that for every year, we expect 
the Olympic gold medal time to drop by just over a hundredths 
of a second. So, over 4 years, we expect it to drop by just over 5 
hundredths of a second. 


Lecture 11: When Life Is (Almost) Linear—Regression 

• However, beware of assuming that every regression line continues 
indefinitely. Clearly, there is some limit at which a human can 
not run any faster. For example, the 100-meter dash will never be 
completed in 2 seconds. 

Applications of Regression 

• So far, we have used only two variables, and we have used one to 
predict the other. But regression can also be a much more powerful 
tool. Regression is often used to determine how strong a correlation 
is. For the Olympic winners, the correlation coefficient (r) was 
-0.91. This is an extremely high correlation value. Depending on 
your field, a much lower value may still give insight. 

• The square of the correlation coefficient (r 2 ) is another commonly 
used statistic. What counts as a “good” r 2 value varies enormously 
from field to field. But the correlation coefficient is where the r 2 
actually comes from. Your regression may also have far more than 
two variables; even 100 variables or more could be involved. Once 
you have the data in place, a regression can be done very quickly, 
even when you have data in more than a few dimensions. 

• Regression is used in various fields, with economics being one of 
the leading areas. One reason for this is because regression enables 
us to artificially change one variable while holding all the others 
constant. For the gold medal times, each year reduces the time by 
about a hundredth of a second. 

• This can be quite helpful in business. Suppose that you have data on 
sales, prices, and promotional activities. Regression can give you 
insight as to what would happen to sales if prices were to increase 
by 5 percent. What if promotional activities were increased by 10 
percent'? This helps marketing. 


Logistic Regression 

• In their very popular book Freakonomics, Stephen J. Dubner and 
Steven D. Levitt look to regression as their tool to bust some of 
the myths about parenting, for example. They use data to see what 
factors correlate to test scores. They’re calculating correlation 
coefficients and seeing which factors have the highest r 2 value. 

• What is correlated? Keep in mind that correlation can be positive 
or negative. Test scores and the fact that the child has highly 
educated parents are positively correlated. Test scores are also 
positively correlated with the fact that the child’s parents have high 
socioeconomic status. But these aren’t all that surprising. 

• We shouldn’t always be seeing what we don’t perceive or expect 
in our world, but we can also get new insight that isn’t as expected. 
This can be the case for some of the factors Dubner and Levitt found 
that don’t correlate—not positive correlation, but not negative 
correlation either. They found that moving to a better neighborhood 
doesn’t mean better test scores, but the possible disruption from 
moving doesn’t hurt test scores. So, there’s no correlation. 

• In addition, the fact that the child’s family is intact doesn’t help 
test scores. On the other hand, the child’s family not being intact 
doesn’t hurt test scores, so there’s no correlation. We often have 
variables like intact versus not intact, and they are just as easy to 
use. In fact, there is another form of regression we can use in such 
cases—it’s called logistic regression. 

• With the Olympic Games, we had two variables: x was the year 
of the Olympic Games, and y was the gold medal winning time 
from that year in the men’s 100-meter dash. But we can also have 
more inputs. Instead of just one input variable x, the studies would 
have used 10, 20, 100, or even hundreds of variables. But all the x 
variables, however many, combine to produce just one y. 


Lecture 11: When Life Is (Almost) Linear—Regression 

• In logistic regression, we still produce a y and have various input 
variables. But with logistic regression, the x values take on only Os 
and Is. They are “on” and “off’: “intact family” or “not,” or “male” 
or “female,” for example. There are just two values. 

Suggested Reading 

Chattier, Math Bytes. 

Levitt and Dubner, Freakonomics. 


1. Download a data set of interest, plot it, and see if the data or data cloud, 
depending on the size, follows some curve. See how you do at predicting 
future events or past events that you exclude from the data. 

2 . Pay close attention to the way we visually identify things. For example, 
we identify handwriting by looking at it and what it looks like. This is 
a key that a computer might be able to do the same thing, as we learned 
in this lecture. Many cameras put boxes around faces or identify when 
people are smiling. This uses similar ideas. What other types of visual 
identification do you see? 


Training Computers to Think like Humans 

Lecture 12 

U sually, data analysts look at data, analyze it, and learn from it. But 
in this lecture, you will learn how computers can be programmed 
to look at data and learn by themselves. The computer—all on its 
own—looks at the data and figures out how to predict what is happening. It’s 
programmed to learn, like our brains. That can sound like science fiction, but 
artificial intelligence is not just a technique used in novels and movies. It is 
used to explore and use large data sets that we may not otherwise understand. 

Artificial Intelligence 

• In the game 20 questions, you think of anything—at all—and your 
partner gets to ask 20 questions about whatever you are thinking 
about that you must answer truthfully. If your partner guesses 
your object in fewer than 20 questions, he or she wins. If he or she 
doesn’t, then you win. 

• This could seem impossible; there are trillions of things to choose 
from. The key is to ask questions that essentially divide the options 
you currently have in half with each guess. 

• This game has been played for over 100 years. It grew in popularity 
during the late 1940s, when it became the format for a weekly 
radio program. But it was transformed by data analytics when 
the computerized game 20Q was invented by Robin Burgener 
in 1988. Today, you can play 20Q online or with the electronic 
handheld version. 

• Initially, 20Q was an Internet game, and it became a sensation. The 
link was e-mailed from person to person. With no promotion, the 
link to 20Q was sent around the world. In fact, this viral success 
played an important role in why 20Q did so well at guessing. 


Lecture 12: Training Computers to Think like Humans 

• How can a computer possibly guess what you’re thinking of? 
How did anyone figure out how to get a computer to make those 
decisions? The computer, in a sense, figured it out. How can a 
computer possibly figure something out? The quick answer is 
artificial intelligence. How does that work? In this case, lots and 
lots of practice. 

• It’s possible that inventor Robin Burgener could have created a 
database containing attributes of common objects. Instead, he 
taught it only one object: a cat. In fact, according to 20Q’s published 
history, the system knew only one question. But he also built into 
the program the ability to learn. Play a game, and it learned from the 
game. So, he began playing, and to help the program learn quicker, 
he put it on a floppy disk and shared it with friends. As each of them 
played, the game learned. By playing again and again and again, the 
program learned more objects, more questions, and what series of 
questions tended to correlate with an object. 

• Then, Burgener shared his program with even more people by 
putting it on the Internet, which helped it get better even faster. And 
as it got better, even more people wanted to try it. That viral success 
meant that it was playing many, many times—and learning a lot. In 
the end, 20Q had built a database of 15,000 objects. 

• According to the 20Q web site, the game guesses correctly within 
20 questions 76 percent of the time and 98 percent of the time if 
you let it ask 25 questions. It can eventually guess correctly, even 
if one or more of your answers does not agree with answers from 
most other people. Even with a vast database, it continues to learn. 

• It’s also interesting that the game could be converted into a handheld 
device that is not connected to the Internet. There would be a 
difference. Rather than that database of 15,000 objects, it would be 
brought down to 2,000. The good news is that people think of those 
2,000 objects 98 percent of the time. 


Neural Networks 

• The 20Q game uses an artificial neural network, which is a 
computational model inspired by the brain. Just like 20Q, these 
so-called neural networks are capable of learning and pattern 
recognition. They’ve been used not only for language, but also for 
computer vision and speech recognition. 

• Amazingly, the work originated in 1943, even before the digital 
computer. Warren McCulloch, a neurophysiologist at the University 
of Illinois, and Walter Pitts, a logician, postulated a simple model 
to explain how biological neurons work. They were worldng to 
understand the human brain. In the end, it provided a foundation for 
computerized learning. 

• When the digital computer became available in the 1950s, the 
ideas of McCulloch and Pitts were implemented as what were 
called perceptrons. They could balance a broom standing upright 
on a moving cart by moving the cart back and forth. The computer 
learned to do this in the same way that a human would—with 
lots and lots of practice, by noting what did and didn’t work 
as it learned. 

• How do we get a computer to learn? A neural network, natural or 
artificial, creates complex behavior from simple units; get enough 
of them acting together, and you can create that behavior. A neuron 
is a single cell that communicates with others. 

A typical neuron in the 
human brain receives 
between 1,000 and 10,000 
inputs from other neurons. 
These signals are relayed 
to the cell body, where they 
combine. If the stimulation 
is high enough, the cell 
“fires,” sending an electric 
signal to its downstream 

Neurons are cells that send and 
receive information between the brain 
and other parts of the body. 


Lecture 12: Training Computers to Think like Humans 

neighbors. The input layer of biological neurons receive their inputs 
from the environment. The axons of neurons relay their signals to 
other neurons, and others are connected to other cells. This is how 
we learn. 

• For a computer, the first layer of the neural network is the input 
layer. In the human body, this interacts with the environment. 
The second layer is known as the hidden layer. It contains the 
artificial neurons. They receive multiple inputs from the input 
layer. Sometimes there is more than one hidden layer. Then, the 
artificial neurons summarize their inputs and pass the results to the 
output layer. 

• So, you give the network training inputs. For 20Q, this is many, 
many people playing the game. You want it to generalize the 
results. The computer is learning, so it can be difficult, if a neural 
network doesn’t work, to fiddle with it. Neural networks are good 
for prediction and estimation when the following are true. 

o The inputs are well understood. (You have a pretty good idea 
of what is important but not how to combine them.) 

o The output is well understood. (You know what you are trying 
to model.) 

o Experience is available. (You have plenty of examples to train 
the data.) 

• A black box model is okay. You don’t need to interpret or explain 
the model. Why did it get what it got? You may not know, but it will 
predict from what it learned. 

• After the online version of 20Q had played one million games, the 
neural network had built up 10 million synaptic connections. Flow 
do they fire and communicate when asking questions? That’s what 
you won’t know. But, clearly, if you’ve played the game, it does 
pretty well. 


Applications of Neural Networks 

• Neural networks and artificial intelligence underscore how 
computers can learn from data. Indeed, they can learn to guess what 
we are thinking, to navigate a room, or to predict various 
phenomena. Neural networks have also been used since the early 
1990s to predict what stocks might rise or fall. A key to effective 
neural networks is training the data—or having it learn on a rich set 
of data. 

• Since the early 1990s, neural networks have been used extensively 
for a wide variety of applications in stocks, from selecting 
or diversifying a portfolio to rating the risk of fixed-income 
investments. Early uses included using neural networks to time 
when to buy and sell stocks. Banks developed neural networks 
to predict interest rates; companies with international operations 
developed neural networks to predict exchange rates. These early 
predictions worked well to attract interest from other organizations. 
As these innovations spread, the expression “neural network” 
somewhat fell out of use. For many users, machine learning was 
becoming just another routine aspect of what computers do. 

Neural networks are used to predict various elements of the stock market. 


© isak55/iStock/Thinkstock. 

Lecture 12: Training Computers to Think like Humans 

• Clearly, it is wonderful when neural networks work. Here also lies 
an important issue in machine learning: We can’t always “open 
the hood” and see the wiring—meaning that you may not be able 
to know exactly what the computer learned, why, and how. If you 
need to know that, a variety of machine learning techniques may 
not suffice. 

• However, if you simply need a predictive method, and you have the 
data and the output you want to predict, then using these techniques 
can be great. All you need to get started with neural networks is 
software that performs neural networks, such as Excel, JMP, SAS, 
or R. Then, you take your data and think about your inputs and the 
output, and then you will have to think about the layers in a neural 
network and the number of neurons. Depending on the software, 
you’ll break the data into three groups: training, validation, and test 
data sets. The validation enables the software to know when it can 
stop learning and consider itself done. It is then ready to be tested 
on the data it doesn’t see. 

• If neural networks don’t work well, there are still a variety of ways 
to improve them. For example, ensemble methods use multiple 
models to obtain better predictive performance than could be 
obtained from any single one. You may not know exactly how 
to build your machine learning program, but you can use several 
versions to build a better one. It is important to recognize that 
machine learning algorithms can take tuning. And, sometimes, you 
simply have to try another approach—which is true in many parts 
of data analysis. 

• Finally, remember that these techniques are focused on a defined 
task. Our minds learn all sorts of things. The 20Q algorithm trained 
from many, many people playing the game and now is able to work 
through various possibilities. This helps underscore that data alone 
won’t allow a computer to learn. The machine must be taught 
effectively. But with the ever-present and growing amount of data, 
there is a lot of interest in learning from the data and automating 
how we can learn and predict from our past. 


Suggested Reading 

Hsu, Behind Deep Blue. 
Warwick, Artificial Intelligence. 


1. Visit the 20Q web page. How does the algorithm do at guessing your 
word? Given your knowledge of how the algorithm works, can you fool 
it? See how someone else does, and then describe how 20Q learned to 
guess, and see if that person can pick a word that falls outside its range 
of experience. 

2 . A fun exercise in artificial intelligence is to look at the world and think 
of tasks that you think could be automated if a computer could learn 
them. This is how many innovations in the field occur. Pay particular 
attention to tasks in which you look at numbers or lists of numbers 
for specific outputs or patterns—even if you aren’t entirely sure what 
pattern you should be seeing. 


Lecture 13: Anomalies and Breaking Trends 

Anomalies and Breaking Trends 

Lecture 13 

I n this lecture, you will learn about a process called anomaly detection, 
which involves trying to spot differences in data. The goal is to find 
objects or behavior that are different from most other objects or behavior 
described by the data. Anomalies, by definition, aren’t easy to find. They 
are the surprises, the exceptions, the countertrends, and the peculiarities. 
As time goes on, more data unfolds, leaving a longer trail for some types 
of anomalies. And research continues to create a more robust toolbox of 
techniques to find these special cases. The good news is that we find such 
anomalies every day, improving our lives and saving money. 


• You might have run into this before: You make a big purchase 
with your credit card, and suddenly you get a call from your credit 
card company checking in. Among the many, many purchases 
happening, how did they see yours, and why did they call? Criminal 
behavior can be detected with anomaly detection. And so can health 
risks—in fact, the techniques for identifying risks to your health are 
quite similar. 

• The goal, in either case, is to find objects or behavior that are 
different from most other objects or behavior described by the 
data—but different only in a relevant way. In data analysis, we 
often won’t know in advance if and when the differences occur. So, 
we don’t know if anomalies are there, but if so, we want to find 
them, or at least flag that something might be happening. 

• At first, this may not seem that difficult to do. Essentially, you are 
looking for an outlier, which might be easy to spot on a graph or in 
a list. But it can be much more difficult to spot all of the anomalies. 


• We need to be careful in thinking that anomalies rarely occur. In 
percentage terms, that will be true. But let’s say that an anomaly 
happens only once in 1,000 times. It will still be pretty easy to spot, 
if you only have 1,000 events. But if what you’re tracking happens 
billions of times, then an anomaly that’s one time in 1,000 may end 
up happening millions of times. We have to remember this when 
working with large data sets. Statistically, an anomaly might be 
unlikely, but it still might occur a million times. 

• Anomalies, by definition, are not common. In the natural world, 
many events and objects we notice and care about are common. Yet 
anomalies are of considerable interest, too—maybe even more so. 

• One thing that is very helpful is to see a variety of forms anomalies 
come in. There are several areas where anomalies are important. 
The first is fraud detection. You may make a big purchase that 
garners a call from the credit card agency—or maybe you’ve made 
a purchase far from home. They call you because that unusual 
purchase is uncharacteristic of you, and they want to ensure that the 
purchase was, in fact, made by you. Noticing a change in behavior, 
in this case in spending, can aid in detecting fraud more quickly. 

• Next is intrusion detection on the Internet. In 2011, a security 
company named Imperva monitored 10 million attacks targeting 
30 different enterprise and government web applications. On 
average, there were 27 attacks per hour, or roughly one attack 
every two minutes. The attacks appeared to mostly be probing for 
vulnerabilities on various sites. If a vulnerability was exposed, 
Imperva found that the automated attacks could grow to upward of 
25,000 per hour—or seven attacks per second. From overwhelming 
or crashing a system to intruding and secretly gathering information, 
attacks on computers happen or are being attempted every second 
of every day. 


Lecture 13: Anomalies and Breaking Trends 

• The third area where anomalies are important is with ecosystem 
disturbances. There can be atypical events that have significant 
impacts on humans, including earthquakes, hurricanes, floods, 
droughts, and heat waves. In these cases, the goal is to predict 
such anomalies. 

• Fourth is public health. These techniques can help with outbreaks. 
The Carnegie Mellon system ran an algorithm to determine how 
likely some anomaly happened by chance. Suppose that normally 8 
percent of cases with patients over 50 involve respiratory problems 
but that today this number is 15 percent. This system could figure 
out that the probability that this happened by chance is 20 percent— 
so, not as unlikely as you might have expected. Therefore, maybe 
this incidence of higher activity can be taken less seriously. 

• Finally, let’s take an example from individual medicine. When you 
go to a doctor, you may have tests done. Generally, you don’t want 
unusual test results. Keep in mind, though, that what constitutes 
an anomaly may depend in part on the age and sex of the person. 
Correctly identifying an anomaly relates to costs, too. Unneeded 
tests can cost money. If a condition isn’t noticed, it can be harmful. 

• If you are working with data, finding anomalies can be helpful. 
But if you are looking for meaningful averages and statistics, such 
things can be an issue. So, sometimes, anomaly detection is part of 
data preprocessing. 


• The causes of anomalies aren’t all the same, and this is important to 
keep in mind. First, data may come from a different class or source. 
If someone has stolen a credit card, he or she is of a different class 
than credit card users. In mathematical terms, a purchase made 
with a stolen credit card is an extreme value. It’s what in regression 
we call an outlier. Statistician Douglas Flawkins’s definition of 
an outlier is as follows: an observation that differs so much from 
other observations as to arouse suspicion that it was generated by a 
different mechanism. 


• Second, there is natural variation in data. A bell curve is a normal 
distribution, and a large amount of data can follow this type of 
curve. Height is often an example. A height of 6 feet 11 inches is 
not very common among men, so this height is at an extreme value 
of the normal distribution for heights. 

• Another source of anomalies is errors in data collection or 
measurement. If the data you collect seems flawed, spend some 
time looking at your data to ensure that it is correct. You may 
be more likely to detect a problem if you have a reasonable idea 
about what distribution to expect in your data—and what would be 
highly unlikely. 

• Sometimes, models are difficult to build. For example, if you don’t 
have data in advance—if you don’t know in advance what things 
are going to look like—then other techniques may be needed. Other 
methods look at other factors, such as how close an object is to 
the others or the density of objects in a region. For example, is a 
credit card purchase wildly different from the value or frequency or 
location of typical purchases? Do you have one huge purchase? Do 
you have a flurry of smaller purchases? Sometimes, these types of 
anomalies can be seen on a graph. 

• One way to detect such an outlier is to use the method of clustering. 
A cluster is a dense glob of dots. A cluster of one element, called a 
singleton, would be quickly identified. If we wanted to measure the 
distance of points, we could measure the distance from points to the 
distance of the center of the cluster, called the centroid. This would 
help us see that the one main is much farther than anything in the 
dense group of points. Clustering finds a group of points in two, 
three, or more dimensions. You don’t need to graph it. 

• There are some very specialized statistical tests to calculate what 
counts as an outlier, at least for specific types of data. Clustering 
is an example from the area of data mining. We might use machine 
learning techniques, from the area of artificial intelligence. Other 
times, you might use statistical techniques, which calculate, among 


Lecture 13: Anomalies and Breaking Trends 

many things, the probability of something occurring. There are also 
other techniques from areas such as information theory and spectral 
theory. A lot of methods depend on assumptions about your data 
that may not be valid. So, you may need to try several methods, 
especially if you don’t know what to expect. 

Advances in Data Analytics 

• Advances in data analytics have a direct impact on how and when 
we can find fraud. There are data sources that were previously 
ignored because they change too quickly. Traditional techniques 
simply couldn’t handle them. The insurance industry is an example. 
They can refresh fraud scoring in real time, but then that entire data 
set changes. Aberrant behavior can be analyzed in employees, too, 
with huge log files from claims or bill processing systems. 

• At one time, analysis of such data would take hours or days to run. 
Now, billions of rows of data can be analyzed in seconds. This 
speed has profound impacts on data analysis. Applications that once 
demanded a sample, or subset, of the data used can now run on the 
entire data set. There isn’t a need to find a representative sample. 
You simply run it on the entire data set to learn and explore it. 

• Furthermore, given the speed of modern algorithms and 
technologies, models can be retuned and tested quickly. If a model 
seems to be failing and reducing in its ability to detect today’s 
fraud, then a refined model can be tested quickly and analyzed. If it 
is effective, it can be deployed quickly. At one time, refined models 
might have been deployed only once or twice a year. 

• The larger lesson is that whatever you might consider today that’s 
beyond your computing resources should be logged for tomorrow. 
Sometimes, in a year or two, something that was impossible 
becomes possible. It may be better to postpone ideas rather than 
write them off entirely. They may, in time, be viable—if you don’t 
lose track of them. Returning to an old hunch with fresh tools can 
be exciting and challenging. 


Suggested Reading 

Bari, Chaouchi, and Jung, Predictive Analytics For Dummies. 
Gladwell, Outliers. 


1. If something is 99 percent likely not to happen, it still happens about 
three times per year on average. Sometimes, we expect unlikely things 
to happen much less than they will. We expect less of a pattern, in a way, 
than will happen. 

2 . When something seems odd, it may just be unexpected to you, or it 
may be an anomaly. If you sense an experience or observation that is an 
outlier, how did you recognize that it is? Are you sure it is? If you learn 
to narrow down how you know, you are thinking like a data analyst and 
honing in on how to do this with data. 


Lecture 14: Simulation—Beyond Data, Beyond Equations 

Simulation—Beyond Data, Beyond Equations 

Lecture 14 

S ometimes, we have too many possibilities to consider, and sometimes, 
phenomena are simply too difficult to capture in an equation. 
Simulation is a powerful tool in our world. In many cases, rather than 
analyzing lots of data, you can produce a simulation and analyze what that 
says about a physical phenomenon. This tool of data analytics allows us to 
make better medicines and faster cars and to explore new realms of scientific 
study—all with the speed and safety of a computer. 

Monte Carlo Simulation 

• The World Series of Poker in Las Vegas is a tournament that begins 
with thousands of competitors and narrows down to the final two 
sitting at a table with hundreds of thousands of viewers tuned in to 
see the outcome. The winner is considered the World Champion of 
Poker and receives a multimillion-dollar cash prize. When the game 
is broadcast on ESPN, a 
card is dealt, and quickly 
the probability of each 
player winning given the 
current hand is updated. 

• How is this done? 

Could there be a big 
database of all possible 
combinations? In that 
case, a card is dealt, 
and someone looks up 
the probabilities for 
that given state of the game. Consider how big such a database 
would need to be—and consider how fast we want our answer. We 
possibly could do it that way, but there is another simpler way: use 
a computer to simulate the game. 

Simulation is a great tool that can help 
you play the game of poker. 


© VIVl/iStock/Thinkstock. 

• To see how, we turn to another card game and travel to Los Alamos, 
New Mexico, in the 1940s. Stanislaw Ulam, while working on 
the Manhattan Project that developed the nuclear bomb during 
World War II, pondered the probabilities of winning a card game 
of solitaire. Because the computations of the probabilities are 
inherently complex, Ulam explored another route. On an early 
mainframe computer that he programmed to simulate solitaire, he 
would play the game a large number of times and computed the 
proportion of times that he won. 

• Such an approach became known as Monte Carlo simulation, 
because the methods often depend on an element of chance, such 
as what cards will be dealt. Today, an ordinary spreadsheet can 
generate and insert random numbers for you. 

• Such methods can be used to simulate more important real- 
world phenomena, too. At Los Alamos National Laboratory, 
Ulam and John von Neumann also used the methods to simulate 
nuclear reactions. Today, Monte Carlo simulation is used to study 
applications in such areas as physics, mechanics, and economics. 

• Let’s return to poker, and specifically the game Texas Hold’em, to 
see how simulation could save us from developing a database of 
trillions and trillions of probabilities. The rules of the game are as 
follows. Two cards are dealt face down to each player. Then, five 
community cards are revealed, face up. Each player takes his or 
her best five-card poker hand from his or her two down cards and 
the five community cards, and the player with the best hand wins. 
During the process of dealing, there are several rounds of betting, 
and much of the strategy in Texas Hold’em comes from betting. 

• Here’s where a simulation can help you play the game. You won’t 
know, unlike the TV broadcasters in the World Series of Poker, 
what hand everyone holds. We want to find the odds of winning 
from a given two-card starting hand, assuming that no players 
fold. These probabilities can be computed quickly on a modern 
computer with simulation. Today, a spreadsheet can do this kind of 


Lecture 14: Simulation—Beyond Data, Beyond Equations 

simulation. Like Ulam, we put the current state of a game into the 
computer. Then, we let the computer play thousands or millions of 
random games and count the fraction of wins, losses, and draws for 
each player. 

• Monte Carlo simulations must be run many, many times. We need a 
lot of numbers—a lot of data—to find what we’re looking for. From 
the law of large numbers in mathematics, as we run more and more 
tests, we will tend toward the expected value. The issue, which 
isn’t a huge one for computers, is that we need to run hundreds of 
thousands of experiments. Then, we begin to see the values that we 
want and expect to see. 

Simulations in Our World 

• Simulation can help us understand our world. It can help answer 
questions in probability that can be difficult to answer. This is what 
Ulam was doing when he simulated solitaire. 

• Another example is the Monty Hall problem. It’s based on the game 
show hosted by Monty Hall, in which you are told that there is a 
100-dollar bill behind one of three doors and that there is nothing 
behind the other two. You choose one of the doors. Then, you are 
told one of the other doors that does not contain the money. At that 
point, you may change your guess to the remaining door—the one 
that you did not choose the first time and that you were not told did 
not contain the 100 dollars. 

• Is it a better strategy to stick with your first choice or switch? 
This question appeared in the “Ask Marilyn” column of Parade 
magazine in 1990. It caught wide attention. The problem was stated 
as having goats and a car behind the doors. In her column, Marilyn 
vos Savant asserted that switching is the best strategy. She got 
thousands of letters, and 92 percent of them insisted that she was 
wrong. She settled the argument with a simulation. She called upon 
“math classes all across the country” to simulate the probabilities 
using pennies and paper cups. She was right, and of course, the 
simulation backed it up. 

• What else can simulation do? Have you been in a fast-food drive- 
through and noticed that they time how long it takes to fill your 
order? Such information can be quite important and helpful. This 
branch of simulation is called queuing theory. If we know the 
rate at which customers arrive and the length of time it takes to 
fill the order, we can simulate queuing up, or lining up, under 
different scenarios. 

• When should you have the cashier take orders only and leave the 
filling of orders to someone else? Simulation can help you 
determine the impact of such choices. You can see what happens on 
average. You can also see the extreme cases, or outliers, and 
determine their frequency and if they are acceptable. 

• Similar concepts allow one 
to model emergency room 
intake to reduce waiting 
times. In traffic studies, you 
can model the difference 
between a roundabout 
and an intersection with a 
stoplight. Simulation is a 
great tool when you might 
change parameters in the 
problem. Sometimes, you 
need an analytical solution 
computed directly from the data, but often, a computed number will 
do just as well. If so, simulation can save a lot of time—and allow 
you to quickly test many more ideas. 

A simulation can help planners 
decide between a roundabout and a 
stoplight for a particular intersection. 

• While it’s only a model, a simulation can, if carefully constructed, 
have enough realistic behavior that it will uncover enough 
characteristic behavior to offer insight. With that, decisions 
can be made. Simulation in general requires some computer 
programming—but not too much. 


Lecture 14: Simulation—Beyond Data, Beyond Equations 

Simulation in Hollywood 

• Simulation can model phenomena in our world. Blockbuster 
movies often contain stunning special effects—particularly 
computer-generated images (CGI). Such images often rely heavily 
on simulation. 

• In the 1980 Star Wars film The Empire Strikes Back, Yoda was a 
puppet controlled by Muppeteer Frank Oz, the one behind Fozzie 
the Bear, Miss Piggy, and Grover. In Episode II, Attack of the 
Clones, from 2002, Yoda was created using CGI. Frank Oz was still 
the voice, but he no longer controlled the movement as he did when 
Yoda was a puppet. 

• In order to operate Yoda in a computer as opposed to the hand of 
a puppeteer, animators create a digital wire frame of the character. 
Such a model can contains over 50,000 vertices connected by lines. 
That number of vertices is needed to capture the detail of Yoda. 

• To move and animate Yoda, animators sometimes simply decide on 
specific places for Yoda’s arm, for example, to be in space and time. 
Then, it is the computers’ job to figure out where that limb will be 
in intervening frames. 

• Animating Yoda’s hair is even more complicated. Unlike the 
movement of his body, the movement of his hair may not be 
specified, except in the first frame of the scene. Generally, it is up 
to the computer to determine how his hair would move given the 
movement of his body. Often, the computer is also figuring out the 
movement of his body. 

• Simulation is used to determine how his hair will move. A model 
is built. In particular, animators model hair as springs. You can 
determine how springy hair is in the model, too. Think of a bed, 
where some springs are bounder than others. Then, you let the 
computer determine this given the force acting on the hair. This 
allows animators to put digital doubles into scenes. By simulating 


the movement of hair, it may not be exact and perfect, blit it is close 
enough that the audience buys into it. 

Simulation in Science 

• Of course, Hollywood is different from science. In science, a 
simulation is used to predict or explain behavior. In the movies, a 
simulation needs only to produce images that give the appearance of 
reality. But simulations in entertainment and science are becoming 
closer, too. CGI simulations for scientific purposes can make it 
easier to visualize large data sets in motion, further blurring the line 
between scientific simulation and entertainment-quality CGI. 

• Simulation not only visualizes imaginary worlds for Hollywood, 
but it can also help us understand our universe scientifically. The 
Bolshoi simulation is a massive, incredibly detailed model of the 
universe’s 14-billion-year history. The images it is producing are 
amazing and being closely studied. 

• The simulations create frame after frame of video. They simulate 
the evolution of the universe. They do this by first examining the 
data from NASA’s WMAP explorer, which maps out the cosmic 
microwave background radiation. That radiation is the light that 
was left over from the big bang. That data can be used as the 
starting conditions of the universe, and then the supercomputer can 
simulate how the universe evolved. 

• While there are reaches of the universe we have yet to explore, 
there are many regions that we do know. So, the supercomputer’s 
results are compared to parts of the universe that we do know. And 
they match up really, really well. 

Suggested Reading 

Gladwell, The Tipping Point. 

Neuwirth and Arganbright, The Active Modeler. 
Shapiro, Campbell, and Wright, The Book of Odds. 


Lecture 14: Simulation—Beyond Data, Beyond Equations 


1. You can think about how to simulate many aspects of life. If you can 
simulate it, what questions might you explore with it? How heavy 
must traffic be for a roundabout to be less effective than a four-way 
stop? Which board games could be simulated, and could you compare 
strategies that people play? This is the first step in building a simulation 
and knowing why are you creating it. 

2 . Many video games are a form of simulation. Video games must have 
a quick response to your decisions. Can you determine what type of 
underlying model the program is using? Sometimes, possibly even 
often, you may not know. But create your own models and play the 
game as a data analyst. 

3. When watching movies, pay attention to the presence of special effects. 
What looks real, and where is the special effect less than real? It can be 
difficult to concentrate on this. You might have to watch the movie more 
than once. 


Overfitting—Too Good to Be Truly Useful 

Lecture 15 

S ometimes data analysis is too good to be true—or, it is too good to 
be truly useful. Data analysis builds models that take data to predict 
future outcomes or explain past events. The goal is to extend a model 
into something we’ve not yet observed and make predictions. To do this, you 
usually have to be less predictive in the past. If we can find that sweet spot 
between predicting the past and predicting the future, then data analysis is 
at its best. It can improve our forecast and give us more time to respond to 
the forecast. 


• In 2014, Warren Buffett offered a billion dollars to anyone who 
could perfectly predict winners of just over 60 games in the NCAA’s 
March Madness tournament. What if someone offered to sell you a 
method that is a system of linear equations, one equation for each 
game teams have already played? It uses all the data available 
from the last 10 years, and it can create a perfect set of predictions 
for all of those years. How much would you be willing to pay for 
this method? 

• Hopefully, this promise sounds too good to be true. Here’s why 
the method described won’t work: If this method includes the 
tournament that you hope to predict in the data, then you already 
have information that you’re trying to predict. If the method 
knows that its job is to predict the tournament and that that data is 
included, it is actually possible for some methods to simply look 
at those games and predict the winner that way, because it already 
knows the outcome. 

• In other words, if you know the outcome of a game you need to 
predict, then you simply won’t pay attention to the outcome of any 
other game. You need to separate training data from data you use to 
test and predict. 


Lecture 15: Overfitting—Too Good to Be Truly Useful 

• This is an example of overfitting data. In this example, it is pretty 
obvious that something went wrong; it is too good to be true. You 
haven’t kept your training data separate from the data you use to 
predict. The model will not generalize at all to new data, because 
the model is overly fitted to past times that will never occur again. 

• Overincluding the past isn’t the only problem. Overfitting can 
also happen if we include a variable that really has no insight for 
the analysis at all. With so much attention and interest, especially 
in presidential elections, there is a lot of curiosity about ways to 
explain the election through something totally unrelated. For 
example, did you ever notice that if the Redskins won their last 
home game before the election, the incumbent party would hold the 
White House? This has been true in 16 of the past 18 elections. 
2012 was an exception. The Redskins lost to the Carolina Panthers 
on November 4 th . But Barack Obama won the election for his 
second term. 

• As we have seen before, it is easy for us to see correlation and think 
causation. This variable, while correlated with past data, doesn’t 
mean it will have predictive value in the future. However, if you 
allow this to enter your data analysis, it could end up looking like 
a highly predictive variable. We often find patterns where there 
may not really be any, and if we throw such patterns into our 
model without thinking, the model may mistakenly tell us that it’s 
actually helping. 

• Too many variables can lead to great results on predicting past data 
but poor performance for future data. Again, we are overfitting the 
past. Said another way, we are trying too hard to predict the past 
when our real goal is to predict the future. In many cases, it is much 
better to have fewer variables than more. 


Ockham’s Razor 

• The idea of striving for fewer variables and less theory in our model 
connects to the principle called Ockham’s razor, which is attributed 
to the 14 ,h -century logician and Franciscan friar William of 
Ockham. The principle states that entities should not be multiplied 
unnecessarily. Many scientists have adopted or reinvented 
Ockham’s razor. A more current way of saying this is that if you 
have two competing models, each making the same predictions, go 
with the simpler model. 

• Ockham’s razor is especially useful as a heuristic in the development 
of theoretical models. When it arbitrates between published models, 
that may be a sign that the theory being rejected wasn’t ready for 
publication after all. 


• The opposite problem of overfitting is called underfitting. As the 
name suggests, underfitting is where things become too simple—so 
simple that they don’t adequately describe the phenomenon. And 
here lies the difficulty: One must make a model complex enough 
that it can predict both the data you have and future data you have 
yet to see, but it must not become so complex that is performs really 
well on current data to the degree that it does not perform well on 
future data. This inherent tension is ever-present. 

• This can sound hopeless. But we do have predictive models. And 
they can save lives. For example, modern data and computing have 
dramatically improved modern hurricane prediction. Underfitting in 
hurricane predictions is no longer the problem it once was. 

• Hurricane forecasting overcomes the problem of underfitting 
with lots and lots of data. Satellites collect information about 
the hurricane’s position, wind movement, and the atmosphere’s 
temperature and moisture levels. This information is improving all 
the time. 


Lecture 15: Overfitting—Too Good to Be Truly Useful 

Scientists learn how and why hurricanes form and strengthen by using 
hurricane forecast systems. 

• This data is used to calculate temperature, pressure, and humidity 
changes usually in 30-second intervals at points on a grid of about 
100 trillion points. That’s a lot of computing. And that’s what makes 
it possible to predict a path two or more days down the line. 

• But there is still room for improvement: Underfitting is still a large 
issue for forecasts about intensity, which are not significantly better 
than a few decades ago. In fact, you may notice that fields where 
there is still plenty of room for improvement will leave more room 
for error by referring to “forecasts” rather than predictions. 

Overfitting and Underfitting 

• In a sense, the worst thing about underfitting is how it leads to 
overfitting. When we don’t have enough data, we overfill the gaps. 
We may have some shiny gems of data in the mix, but that dazzles 
us into thinking we know more than we do. Overall, it’s hard to be 
fooled by underfitting. Your training data and your test data give 
poor results. Your model doesn’t work, and you immediately know 
it doesn’t work. 


Joe Raedle/Getty Images Newsffhinkstock. 

• But one must be more careful to avoid overfitting, which can occur 
from more than one direction. Sometimes an entire variable or 
model can cause overfitting. Another culprit is not appreciating the 
place of noise, or error, in real data; you must assume the presence 
of measurement error. 

• What if we wanted 100 percent accuracy for the data that we 
measured? One bad possibility is just to change the data. For 
example, Gregor Mendel, the famous 19 ,h -century heredity 
researcher, published data about heredity in peas that was much 
later found to be too perfect. Decades later, a statistical analysis of 
Mendel’s data by R. A. Fisher found there to be too little noise in 
Mendel’s result. We always want to avoid overfitting data, even if 
it’s for a cherished model. 

• Another way to strive for 100 percent accuracy is to overfit the 
model itself: We could leave the data alone and make too many 
adjustments to the model. But, remember, data is generally not 
exact and can contain spurious components. It may help to think 
of static in a phone call. When someone calls and has static in the 
connection, you try to hear the person talking. So, you work to 
listen to the voice within the static of the call. 

• In his book The Signal and the Noise, Nate Silver lays out a case 
for the Fukushima nuclear disaster as a dangerous outcome of 
overfitting. Earthquakes continue to be unpredictable. Even with 
today’s supercomputers, modern geophysicists are only marginally 
better at predicting than simple historical means were. 

• With hurricanes, we can collect data—lots of it. With earthquakes, 
relevant data is much harder to collect. We cannot directly measure 
stresses 20 kilometers or more underground. So, the data is scarce, 
and underfitting is an issue. In addition, the current data we have 
is approximate—that is, it is also noisy. So, we’re at risk for 
overfitting what we do have. 


Lecture 15: Overfitting—Too Good to Be Truly Useful 

• If we look over long geological timescales, it turns out that 
earthquakes are far from random. You can even fit the data well 
with an underlying function. Then, you can pick any point on the 
Earth and compute a future earthquake probability. 

• So, it depends on what you are studying. A hurricane may be 
predicted within hundreds of miles based on short-term factors that 
have become easy to measure. Earthquakes, by contrast, depend 
on data occurring not only deep in the Earth, but also spread over 
decades, if not centuries. The kind of data needed is different, and 
adjustments needed for lack of data are also different. 

• Noise isn’t the only issue in overfitting. It is possible not to have 
enough data. And it is possible to have too little noise. In such a 
case, you actually may not have enough noise to accurately predict 
what is happening. If you have only two points, you will fit the data 
exactly with a line. Or, if you have only one point, then you can’t 
draw any line at all. But if you can get 10 to 20 points, then you are 
not overly weighting just one or two initial measurements. And if 
you can get a few hundred points, or a thousand, then you are not 
overly weighting a couple dozen points. 

Suggested Reading 

Berry and Linoff, Data Mining Techniques. 
Silver, The Signal and the Noise. 


1. Remember that overfitting can be caused by including too many 
variables. When you think of phenomena of interest that you’d like 
to predict, think not only about the variables that you think will be 
predictive, but also what you don’t expect to be predictive. What do you 
think you could drop? When building models, it is often good to start as 
simple as possible and see what meaningful information comes out, and 
then build up from the basic levels. 


2 . If you hear a result that seems too good to be true, check if it is 
describing past events or happened only once. Methods can be overfit 
or can simply get lucky due to randomness. Remember, if you have 70 
people, someone is likely to flip five heads in a row. They aren’t a better 
heads flipper; you just have enough people to make that probable. 


Lecture 16: Bracketology—The Math of March Madness 

Bracketology—The Math of March Madness 

Lecture 16 

E very March, the United States goes entirely mad for March Madness, 
an NCAA Division I basketball tournament. In 2012, it was estimated 
that 86 percent of employees devote part of a workday in March to 
the tournament. And this was before Warren Buffett offered a billion-dollar 
prize to whomever could correctly predict the entire tournament. Once you 
know how to create brackets driven by data and appropriate weighting, you 
can apply this method to any subject—and come up with a winner that may 
surprise you. 

March MATHness 

• For March Madness, our tool of choice is bracketology, where we 
use math to fill in a diagram of brackets. For NCAA basketball, 
things begin on a Sunday in March with the announcement of which 
teams are in the tournament, along with the first-round matchups. 
There are men’s and women’s tournaments. 

Blank March Madness Bracket 


• Then, it’s your turn. Who do you predict will win the first-round 
games? From there, you choose who will win in your predicted 
second-round matchups. You keep selecting winners for each round 
until you have selected a national champion. This creates your 
bracket for the tournament. 

• If you are in a pool, how do you know who wins? It really depends 
on how you keep score. Some pools award each correct prediction 
the same number of points; others give more points for correct 
predictions in later rounds of the tournament. This is what the 
sports network ESPN does. 

• There are more than 9 x 10 18 possible brackets, or more than 9 
quintillion brackets. So, we can’t simply pick a random bracket and 
hope for the best—the chances are against us. If we wanted to win 
just by guessing, our chances would be better in a lottery, with odds 
of just a billion to one. 

• How can we do better than mere chance when we predict our 
winners? The math we’ll learn will rate every team in the 
tournament from the best to the worst. We’ll follow the math and 
predict that the team with the better rating always wins. In fact, we 
will use methods previously used to rank teams in NCAA college 
football, which historically has had holiday bowl games instead of 
a full tournament. 

• Let’s use some real data from the 2012-2013 season. For now, let’s 
work with winning percentage. Simply take a team’s total number 
of wins and divide by the total number of games. If a team won 
2 and lost 3 games, then that team has a rating of 2 divided by 
5, which equals 0.4. The higher the rating, the better the team is 
predicted to perform. 

• We’ll use the result of every game between Division I men’s 
basketball teams leading up to March Madness. Even though we 
will only rank the 64 teams in the first round, we will see how they 


Lecture 16: Bracketology—The Math of March Madness 

played against everyone to find our ranking. This amounts to 5,000 
games. Because we look at every game, we actually rank every 
Division 1 team, which is about 350 teams. 

• This data is gathered and made available on web sites, such as This is game-by-game data. Massey’s site 
gives you the date of the game, who played, if either team played at 
home (because sometimes games are at a neutral location), and the 
score of the game. To rank the teams, you have to convert the raw 
data into a linear system, which you probably won’t do by hand. 

• Winning percentage doesn’t take into account the strength or 
quality of the two teams playing, so the results you get using 
this method are not much better than flipping a coin. It’s not just 
whether you win or lose, but whom you play against. So, we can 
design mathematical methods that integrate strength of schedule by 
adding weights to rank each team. 

The Massey Method 

• It will help to learn a method used by Professor Kenneth Massey 
of Carson Newman College to rank NCAA college football teams. 
Early in the 2012 season, team A beats B by 14 points, and team B 
beats C by 3 points. Can we now predict that team A will beat team 
C by 14 + 3, or 17 points? Of course not. If we could, scores would 
be what is called transitive. 

• So, if transitivity doesn’t always hold, we can only assume that it 
holds approximately. If a team wins one game by 61 and another by 
59, then we can approximate with 60. In data analytics, assumptions 
are often made. You assume a property, and as long as it is close to 
holding, insightful information emerges. 

• Let’s see how this assumption leads to a linear system that produces 
rankings of teams. Let’s look at 3 teams: x, y, and z. We need a 
rating for each. Assume that team x beats team y by 10 points. 
The Massey method, as it has come to be known, writes this as 
x — y = 10. This means that x wins, y loses, and 10 is how much x 


won by. And we can add rating 5 for each team. So, if team x has 
a rating of 13, then x = 13. If team y has a rating of 3, then y = 3. 
So, team x would be predicted to beat team y by 13 - 3 = 10 points, 
which we can write asx - y = 13 - 3 = 10. 

• We don’t know the ratings of the teams. That’s what we are finding. 
But we do know the difference in points in the games. So, if we 
have a game where team x beats team y by 10, then we form the 
system x — y = 10. Assume, additionally, that team y beats team z 
by 5 points, which would give us y — z = 5. Finally, suppose that 
z - x = 1. Team z won that game. 

• We can quickly see that transitivity didn’t hold. Team x beat team y 
by 10, team y beat team z by 5, but team x didn’t even beat team z, 
but instead lost by 1. So, transitivity doesn’t hold. That means that 
we won’t be able to find values for x, y, and z that exactly solve this 
linear system. 

• In fact, we are usually going to have quite a few more games than 
the number of teams. Again, for March Madness, we have around 
5,000 games and about 350 teams. This means that we have more 
equations than variables. 

• For our type of problem, we can approximate the equations using 
what is called the least squares method. Instead of averages, we 
square each of our numbers, add them up, and take the square root. 
It’s like a giant Pythagorean problem, but computers quickly and 
easily solve such problems. So, it is a matter of entering numbers 
into a computer and pressing enter. 

• This is going to be a system—in the case of March Madness, 
with 5,000 equations, one for each game. We can, though, form a 
smaller system with only 350 rows, one for each team. That’s still 
a lot, but it’s quicker, and it’s also how Massey has done it. Many 
programming languages have a function that performs a linear solve 
directly, but even in a spreadsheet like Excel, this can by done. 


Lecture 16: Bracketology—The Math of March Madness 

• We are using the version of the Massey method that considers 
scores, but you don’t have to include scores. In fact, when NCAA 
football used Massey’s method, they didn’t do it with scores, due to 
their concern that weights based on scores could reward blowouts. 
A big win in one game against a weak team could increase a team’s 
overall rating. This method could also be adapted to look only at 
wins or to dampen the reward for large wins. 

Using Mathematical Software 

• For March Madness, the linear system we create will have about 
350 equations and 350 unknowns. A computer can solve this 
system very quickly. We might use a command like LinearSolve on 
the web site Wolfram|Alpha or “linalg. solve” in Python, which is a 
free programming language. 

• You can use mathematical software called Matlab, or you can use 
Excel, but you might not want to use Excel if you’re working with 
a rather large system. Sage is a free mathematical software that it is 
often described as a combination of Matlab and Mathematica. You 
can also code it in Java, but Java doesn’t solve linear systems as 
easily as other languages. An advantage with Java is that it allows 
you to post codes on web pages. 

• So, we can take all the data from a complete season of Division I 
men’s (or women’s) basketball season and create the ratings. Once 
we have the ratings, it’s easy: The higher the rating, the better we 
considered the team. Then, we create a bracket by choosing the 
higher-rated team in every matchup. 

• If we submit this score-weighted bracket to the ESPN Tournament 
Challenge, we find that it beats over 73 percent of the over 8 
million brackets submitted. That’s a very, very stark increase over 
our winning percentage bracket, which only beat 1.8 percent of the 
brackets and was competing with coin flipping. 


• We’ve added weights based on scores, but we haven’t yet decided 
how much to weight each game. The key is determining the 
importance of a game. There are many, many ways to weight the 
games in a season. You can weight games higher if the team won the 
previous game. This weights a team’s ability to win consistently— 
to sustain a winning streak. You can weight games that are won on 
the road, because essentially every game in March Madness is on 
the road. 

• Another option is breaking a season into four parts. Then, we 
decide how to weight each part. We could count the games in the 
first quarter of the season as half a win and loss for the respective 
teams. In the second quarter of the season, the games could count 
as 0.75 a game. How do we weight games in the third and fourth 
pieces of the season? Is the last part of the season leading into the 
tournament the most predictive of a team’s success? If so, maybe 
you could weight it as 1 game or even 2. 

• How do we use this when we form the linear system? It’s pretty 
straightforward. You simply count the number of weighted games. 
So, once we determine our weighting, we can form a revised linear 
system. Once that’s formed, a computer can produce the rating, and 
we are ready to form a bracket. 

Suggested Reading 

Langville and Meyer, Who s #1 ? 
Oliver, Basketball on Paper. 


1. Several professors have used sports-ranking methods to rank teams only 
by their play against each other to predict end-of-season conference 
play. This makes for much smaller systems. How predictive can you be 


Lecture 16: Bracketology—The Math of March Madness 

without using a system of 350 unknowns? You may want to create a pool 
with friends who don’t use math to see how you compare. Remember, it 
can depend on the variability, or “madness,” of the tournament. 

2 . You might want to create three brackets and see how they compare. 
The first bracket is created before running any of the math methods. 
The second is created using only the math methods and letting it choose 
the winners. The final method takes the math and overrides some of its 
decisions. Which does better? Try this over several years and see if any 
particular one tends to do better. Does one do better in early rounds than 
another? What about later rounds of the tournament? 


Quantifying Quality on the World Wide Web 

Lecture 17 

S earch engines like Google play a huge role in our world. The 
importance of search engines also makes them big business. In this 
lecture, you will leam the data analytics of search engines. Google 
has stayed relevant, from the time when the Internet had millions of web 
pages and into the era of trillions of web pages. The reason that the Google 
algorithm has continued to work for more and more users is because the 
algorithm itself adjusted to downgrade attempts to hijack search results, 
while also finding evermore ways to deliver meaningful results. 

Google’s Algorithm 

• There are hundreds of billions of web pages. But someone can still 
manipulate a system of that size by knowing how Google analyzes 
that system to form its search results. The PagcRank algorithm 
quantifies the quality of a web page. The quality of a web page 
helps determine which results are listed earlier in the results from a 
search engine. If two web pages are equally relevant to a query that 
you input, then the page with higher quality is listed first. 

• The ability to quantify quality of web pages is part of what allowed 
a once-struggling Internet company, Google, to overtake their 
competitors and become the most visited web site in the world. 
The founders of Google were Sergey Brin and Larry Page, graduate 
students in computer science at the time they started creating 
what became Google. When Google’s algorithm was unveiled 
to the world, there was Google, with its new algorithm, and then 
everyone else. 

• The Internet before Google was a rather tangled web. Millions of 
web pages existed, and the other search engines weren’t as helpful. 
You could go to a search engine, input your query, and, just like 
today, get a list of web pages. But, at that time, the web pages at the 
top of the list often weren’t very useful. It wasn’t uncommon for a 


Lecture 17: Quantifying Quality on the World Wide Web 

much better page to appear farther down the list. This almost forced 
you to look at several pages of search results. The way Google 
analyzed the World Wide Web changed everything. 

• What did Google do that was different? They looked at the 
connectedness of the web. This was part of their billion-dollar idea. 
They didn’t just look at the content of web pages; they also looked 
at the structure of the web. 

• A fundamental idea in Google’s search engine is the concept of 
endorsement. Higher-quality web pages tend to be linked by other 
high-quality web pages. How did Brin and Page determine this? 
Their model can be seen as recommendations, but also as a model 
of surfing the Internet. It’s a model, so it won’t exactly replicate 
how people surf the web. In fact, it’s entirely possible that no one 
person surfs by the rules of the model. But the model captures 
enough of the characteristic behavior to return meaningful and 
reflective results. 

• Because all people are different, the model assumes that surfing 
is random. So, a random surfer moves through the web following 
very simple rules. You can think of it almost like a game. At each 
web page, the surfer rolls a die to decide which web page to go to 
next. Which link should the surfer follow? Two people might have 
different preferences on which link to click, so Google assumes that 
you pick a random link on a web page. 

• But how does this produce a measure of the quality of a web page? 
Brin and Page calculate the probability of the random surfer being 
at any given web page if this model is followed. That probability is 
the associated measure of quality. You can try surfing the network 
you see on the screen to determine which is the highest-quality web 
page under this model. 

• Once this model is set up and there is a network of web pages, you 
simply randomly surf the network following these rules. But you 
must do it long enough that the results settle down. You need to 

surf for a very, very long 
time. How does Google 
know that eventually the 
numbers will settle? A 
nice feature of Brin and 
Page’s algorithm is that 
it will converge for any 
web network—anything 
ever created. 

Google’s search engine uses the idea 
A web page with no links of endorsement to analyze the World 

v . ,, , , .. Wide Web. 

on it is called a dangling 

node. PDF files, movie 

files, and music files are often dangling nodes. If you end up at a 
web page with no links on it, what do you do? You either input 
a new web address or hit the back button. Currently, our model 
assumes that you’re stuck. It’s almost as if you’d give up and 
simply close the browser, because there is nowhere to go. But we 
don’t do that. 

• Google, of course, assumes that you go somewhere else. Where? 
Again, that is assumed to be a random choice. You go anywhere on 
the Internet with equal probability. This relates to the final aspect 
of the algorithm. We don’t always follow links on web pages; 
sometimes, we simply decide to go somewhere else. Brin and Page 
called this teleporting. You teleport either when you are at a web 
page, possibly with outlinks, or you teleport because you are at a 
web page with no links from it. 

• How do we know when to do what? You can think of it as a game. 
At each web page, you roll a die. If it comes up with 1 through 5, the 
surfer will click a link on the current web page and follow that link 
to a new web page—each link is equally likely to be chosen. If the 
die comes up 6, the surfer will go to any web page on the Internet; 
again, each web page is equally likely to be chosen. Finally, if you 
end up at a dangling node, you teleport to any web page with all 
choices equally likely. 


hillaryfox/iStock Editorial/Thinkstock. 

Lecture 17: Quantifying Quality on the World Wide Web 

• That’s the model that Brin and Page created that started Google. 
There is only one difference: Their model assumed that you were 
85 percent likely to follow a link on a web page. By rolling a die, 
we made that 86.6 percent likely. 

Google’s Beginnings 

• Do you surf like this? Probably not. You’re probably not equally 
likely to go to any web page. It’s estimated that there are more 
than a trillion web pages. The subset that you would visit or want 
to visit is very, very small relative to this size. You probably don’t 
even follow links on a web page 85 percent of the time. Then, 
how did Brin and Page know that this model would lead to good 
search results? 

• This was probably the question that the existing search engines 
asked. In the 1990s, blue-chip venture capital firms, Yahoo!, Alta 
Vista, and many other major companies were approached by 
Stanford with Brin and Page’s algorithm. They turned down the 
chance to buy Google’s search system for 1 million dollars. This 
search system became the foundation of the Google company, and 
by the summer of the 2005, each of the founders had a net worth of 
more than 10 billion dollars. 

• David Filo, a Stanford alum and founder of Yahoo!, encouraged the 
pair to start their own company and return when the product was 
fully developed. So, in 1998, the two left their doctoral programs 
and moved their computers into the garage of a friend. 

• Before long, Google became not just a business, but a verb—with 
people saying, “I’ll google that.” By integrating the connectivity 
of web pages, Brin and Page folded in a very important piece of 
information that set their search engine apart. 


Perron’s Theorem 

• How exactly does Google know that the algorithm will stabilize, or 
converge, given enough time? No matter how long we surf, could 
the number one site change if we double the number of steps we’ve 
surfed? A math theorem called Perron’s theorem guarantees that 
Google will always find a unique answer for any configuration of 
the web. It turns out that teleportation enables this. 

• If there is some nonzero probability of surfing from any web page 
to any other web page, then we are guaranteed to find an answer— 
and guaranteed for that answer to be unique. We must have those 
nonzero probabilities for Perron’s theorem to hold. We do, we have 
our unique answer—guaranteed regardless of any configuration 
of the web. That’s a huge result, and teleportation guarantees that 
this happens. The probability may be quite small, but it is nonzero, 
which is all we need. This tiny aspect of Google’s model guarantees 
that the algorithm will always work. 

Internet Politics 

• Brin and Page developed a powerful model that created the 
company Google. But with such prominence comes much attention. 
In particular, companies want to be at the top of the web searches. 
PageRank can help raise a web page’s spot in a list of search results. 

• If you can get web pages with high PageRank to link to yours, then 
your PageRank rises. So, some companies go into business offering 
to help raise your PageRank. One way is by developing pages with 
high PageRank. Then, for a fee, they link to yours. 

• Link farms are created by what are called spammers to essentially 
fool search engines like Google and raise the rank of a web site. 
Generally, a link farm has several interconnected web sites about a 
popular topic and with significant PageRanks. The interconnected 
nodes then link to a client’s page. 


Lecture 17: Quantifying Quality on the World Wide Web 

• When Google figures out that a site sells links, then their PageRank 
gets hit. For example, in 2007, Google decided that some web pages 
were selling links and lowered their rankings. It isn’t illegal to sell 
links, but if you get caught, Google can penalize you. This can have 
a major impact on your business. 

• PageRank isn’t the thing Google uses to create search results, so 
presumably one can exploit other aspects of their search, too. A key 
is knowing what is being done. In November and December 2003, 
if you put “miserable failure” into Google, the official White House 
biography of the President was returned as the highest-ranked 
query. Somehow, this was happening, even though the words 
“miserable failure” were nowhere on that web page. This is what 
became known as a Google bomb. 

• The architect was George Johnston, who had starting building this 
one a month earlier. Putting together a Google bomb was relatively 
easy; it involved several web pages linking to the White House 
biography of the President with an agreed-upon anchor text, which 
is the hyperlinked text that you click to go to the linked web page. 

• In the case of the “miserable failure” project, of the over 800 links 
around the web that pointed to the President’s biography, only 
32 were part of the Google bomb that used the phrase “miserable 
failure” in the anchor text. But from November through December 
of 2003, if you put the words “miserable failure” into Google, the 
top search result was the White House biography for President 
George W. Bush. 

• What part of the Google algorithm did this exploit? Google wasn’t 
just looking at the text on a web site to determine its content. It 
treated the hyperlinked anchor text that links to a site as a summary. 
For example, if you link to The Great Courses, you might link 
with the words The Great Courses or The Teaching Company. 
These words summarize the content. By using such words, one 
can connect the web page to words that possibly don’t appear on 
the site. 


• Google has filed patents and taken other steps aimed at reducing 
the impact of Google bombs. The history of changes to the Google 
search engine offers more general lessons for data analytics. 
The core approach was very good, but Google stayed on top by 
continuing to make the algorithm work better and better. 

Suggested Reading 

Langville and Meyer, Google s PageRank and Beyond. 
Vise, The Google Story. 


1. When you conduct searches on Google, it can be fun to think about 
whether you agree with PageRank’s measure of quality. Would you rank 
one page over another? When you don’t agree with PageRank, is that 
just your taste, or do you think it would be broadly preferable? It can be 
an interesting exercise to think about how to capture such a preference 
in modeling. 

2 . It is interesting to try different search engines and see the difference 
in the order of the web pages returned. It’s also an interesting mental 
challenge to think about what algorithm or modeling decisions might 
account for these changes. You may not, and probably won’t, know, but 
you can improve your ability to think like a data analyst even without 
the answer. 


Lecture 18: Watching Words—Sentiment and Text Analysis 

Watching Words—Sentiment and Text Analysis 

Lecture 18 

W e are approaching a time when every book ever written is 
available in a digital form that is both readable and searchable. 
We are now digitally creating, storing, and analyzing many, 
many more words than appear in traditional publications. All of these words 
offer rich data sets to understand and predict phenomena in our world. The 
challenge is that all of this data is unstructured, so in this lecture, you will 
learn about how to watch the words and gain insight from unstructured data. 
These are the realms of text analysis and sentiment analysis. 

Text Analysis 

• We all have had some experience with text analysis. At the simple 
end, think of spelling and grammar checks. These already contain a 
level of text analysis. They tell you how many words you have, and 
many programs will also autocorrect. 

• Authorship is a traditional area for text analytics. For example, 
you could decipher who wrote the works of Shakespeare without 
knowing that it was, indeed, Shakespeare. You can also look at the 
frequency distributions of individual letters, which are important 
in cryptography, where the distribution of letters can make some 
ciphers too easy to break. 

• As for word frequencies, at the end of 2010, Google released an 
extraordinary tool that looks at frequency distributions across 
millions of published books—all at once. You put in a word or 
phrase, and it looks for the frequency across all of Google’s scanned 
books. The tool is called Google Books Ngram Viewer, and it’s 
an astonishingly powerful tool. You can drill down to search the 
underlying publications year by year. You can refine your search 
in all sorts of ways, to include what you want and exclude what 
you don’t. 


Google Books Ngram Viewer analyzes data across millions of published books. 

• What about social media? On January 8, 2012, Denver Broncos 
quarterback Tim Tebow threw the longest overtime touchdown 
pass in NFL history, ending the game (and the Steelers’ season) 
in incredibly dramatic fashion. The fervor in the Denver stadium 
created an Internet tsunami. USA TODAY reported that thumbs 
were typing at a rate of about 9,400 tweets per second on that night 
as football fans tweeted about the victory. 

• We can look at the volume of posts, which can be huge. But what 
about the words themselves? Researchers at IBM and elsewhere 
have programmed computers to automatically generate journalistic 
summaries of soccer matches using only Twitter updates. Spikes 
in the volume of tweets on a topic help identify key moments in 
the match. No reporter was sent to the soccer match. In a sense, 
the people tweeting were the only reporters, and data analysis 
synthesized the information. 


© Google Books Ngram Viewer, 

Lecture 18: Watching Words—Sentiment and Text Analysis 

• One can write a program to grab the data straight from Twitter using 
what is called an application programming interface (API), which 
defines ways you can retrieve information—in this case, from 
Twitter. You define a function that calls for username, number of 
re-tweets, or whatever information you want and have appropriate 
access to. Some infonnation may require a user password, blit 
often, you can do a lot even without that. 

• There are web sites that offer to download data for you. For 
example, offers the ability to enter search terms and 
download the data live. 

Sentiment Analysis 

• Social media can do more than summarize an event. In 2010, a 
Facebook page created in Egypt called “We Are All Khaled Said” 
gathered 250,000 followers within three months, and the swelling 
sentiment that began online culminated in street rallies and a change 
of regime for the entire country seven-and-a-half months later. 

• But there are some issues with sentiment analysis. First, accuracy 
is never perfect. How well a computer can assess sentiment is 
generally judged by how well it agrees with human judgments. 
But humans don’t always agree with each other. This is 
sometimes called inter-rater reliability, and you can never expect 
100 percent accuracy. 

• Second, data sets and documents vary in their amount of sentiment. 
A lot of content lacks sentiment. One web page may have a lot of 
comments, but it might have a less-charged discussion than another 
web page with fewer comments. So, the quantity and character of 
sentiment may be independent of one another. You may also get a 
different picture using blogs compared to Facebook, and both may 
be different from Twitter. 


• Third, words don’t always mean the same thing. The word “bad” 
famously can mean “good,” as in “Man, that was a bad song!” This 
is important for companies, because they watch social networks 
for sentiment on their products. There may be different lingo, or 
different inflections, for those who market video games than for 
those who market men’s suits. Furthermore, some words are simply 
difficult to track. 

• The fourth issue for sentiment analysis is getting a baseline. 
What counts as negative? Some things simply get a higher level 
of negative responses all the time. For example, a president with 
an 80 percent approval rating would be doing very well. But what 
if a company is seeing 20 percent negative sentiment about its 
product—is that bad? It’s not so bad if all of its competitors are 
creating 30 percent negative sentiment. On the other hand, if 20 
percent negative sentiment translates into 20 percent returns of the 
product, that could be very costly. 

Search Engines 

• Let’s turn to how a search engine determines which web pages 
are more and less related. This involves a vector space model 
and linear algebra. A vector space model relies on two things: a 
list of documents and a dictionary. The list of documents is a list 
of the documents on which you can search. The dictionary is a 
database of key words, which could be some or all of the words 
in the documents. (Sometimes, it’s not computationally possible 
to include all of the words.) Words not in the dictionary return 
empty searches. 

• We then translate all the words into a table of numbers. Each 
row in the table is a key word in the dictionary. Each column is a 
document. The entry in a particular row and column is a 1 if that 
key word is in the corresponding document. Otherwise, the element 
in the table is 0. This is often called the document matrix. 


Lecture 18: Watching Words—Sentiment and Text Analysis 

• Even with this matrix, there are computational issues. Should every 
document be searched, in its entirety, for each key word? Because 
of the sheer size of the Internet, some search engines read and 
perform analysis on only a portion of a document’s text. They read 
only so many words and index from those. 

• But this isn’t the only issue. Do you require exact matching? Will 
you allow synonyms? If you don’t, then you are requiring searches 
to have the exact words that appear in the document. Then, there is 
the issue of word order. For example, do we treat queries of “boat 
show’’ and “show boat” as the same or as different? All of these 
choices impact which documents are deemed most relevant. 

• What can we do with an ability to find similarity between texts? 
Imagine being a legal firm tasked with looking at a large data 
set. How would you begin? Clustering analysis can’t resolve 
everything, but it can find groups and speed up the process of 
grouping information that could unlock important aspects of the 
case, all just by clustering the words in e-mail messages. 

Applications of Text and Sentiment Analysis 

• Sentiment analysis is a big deal in data analysis. It allows companies 
to hear their customers in unprecedented ways. They can hear you 
when you aren’t even talking to them. You aren’t calling Customer 
Service, but Customer Service is still listening. If you post on 
Twitter, they hear it and might even respond. Getting this type of 
information can be worth big money. 

• Today, we can find sentiment available on many topics. When you 
are thinking about buying a new product, you can look around the 
web for online reviews posted by people. You are, in your own 
way, sifting through the data and looking for sentiment. A computer 
that is programmed can zip through many, many web pages, and if 
trained in the right way, the idea and outcome is the same. This is a 
rich resource for businesses. 


• Text analysis can also be used in science and applied fields of all 
kinds. Consider that more than 8,000 scientific papers are published 
every week on Google Scholar, and top-down classification systems 
increasingly do not capture every way of looking at published 
results that could bring insight. 

• Also, ironically, research published in academic journals is among 
the most difficult for ordinary users, or even researchers, to 
access in bulk. To address this problem, in 2004, the University 
of Manchester began hosting Britain’s National Centre for Text 
Mining, the world’s first publicly supported center for text analysis 
in the world, with an initial focus on biomedical information. 

• Biology is a huge area for textual data analytics. For example, a 
project to link locations on the human genome back to specific 
research articles about those locations is underway at a project 
called text2genome. Every scientific field could potentially have 
this sort of mapping between research conclusions and the research 
where those conclusions were proposed and confirmed. 

• There is also fun and creative analysis going on with texts such 
as cookbooks—what you might call recipe analysis. In fact, even 
IBM’s Watson team has gotten into recipes. Their work uses not 
only the text of recipes, but also other data contributed by expert 
chefs to create new recipes. In fact, ways of combining text analysis 
with other tools will probably become more prominent in the future. 

Suggested Reading 

Berry and Browne, Understanding Search Engines. 

Montfort, Baudoin, Bell, Douglass, Marino, Mateas, Reas, Sample, and 
Vawter, 10 PRINT CHR$(205.5+RND(1));: GOTO 10. 


Lecture 18: Watching Words—Sentiment and Text Analysis 


1. Sentiment analysis is an active area of research. Look for it in the news, 
or look for news stories on it periodically to see what is happening lately. 
Keep up with the field, and when you learn of new research, see if you 
can identify new products or features in software that take advantage of 
it, or think about how you would use it in products. 

2. When you look at tweets, text messages, or e-mails, how do you discern 
the sentiment? When you misread someone’s sentiment, why does 
this happen? 

3. Word clouds are a very simple version of text analysis. Online software 
will allow you to cut and paste words into a web page and create 
a word cloud. Try it. Do you agree with the sentiment that the word 
cloud is conveying? Can you create examples where this simple version 
of graphical data analysis would be misleading? Remember, data 
analysis won’t tell the whole story and sometimes can easily convey a 
misleading one. 


Data Compression and Recommendation Systems 

Lecture 19 

D ata compression is powerful: It allows us to store, access, and use 
far more images. But the mathematics inside data compression 
is even more powerful and can be used in other ways, too. In 
particular, you will learn that the same methods for throwing out data can be 
adapted to improve an online recommendation system. The mathematics of 
data reduction makes it possible to decompose a recommendation based on 
thousands of people. Of course, the recommendations work best if you offer 
some ratings yourself. Data analytics can then predict not just whether you’d 
like a particular movie or not, but also what score you might give it. 

The Netflix Prize 

• Netflix put together a million-dollar competition to improve their 
recommendation system. To enter the millionaires’ club, you needed 
to do much more than recommend a film to a user. In a sense, you 
had thousands and thousands of people telling you the films they 
like and how much they like them. It came via the Netflix ratings 
data. To win the cash, your computer algorithm had to do a better 
job of predicting than Netflix’s existing recommendation system at 
the time, called Cinematch SM . 

• Your goal was to take Netflix’s data set of users ratings, which are 
integers from 1 to 5, for all movies. From that, predict how a user 
would rate other films. Note that this is more difficult than just 
recommending. You are actually trying to predict how much a user 
would like it. 

• In the competition, Netflix supplied data on which you could test 
your ideas. When you thought you had something, you’d test your 
recommendation method by predicting ratings of movies and users 
in another data set. Your recommendation system had to predict 
future ratings. So, the second data set had actual ratings of movies, 
and you’d test your predicted ratings against the real ratings of 


Lecture 19: Data Compression and Recommendation Systems 

movies. If your predictions were at least 10 percent better than the 
recommendation system Netflix’s used, Netflix would write a check 
for a million dollars. 

• In data analysis, you need to know the nature of the data. Netflix 
gave you users and movies. One way to store this is in a table. A 
column is one user’s ratings for all the movies, with a 0 being a 
movie that wasn’t rated. So, a row contains all the ratings for one 
movie. We connect a user and a movie with a line if the user rated 
the movie. We associate a number with the line, where the number 
equals the rating of that user for the movie. 

This type of diagram is called a bipartite graph. The lines are called 
edges. For this application, the numbers or weights represent the 
number of stars a Netflix 

user gives it. For another 
application, the weights 
could be a 1 for a thumbs- 
up or a -1 for a thumbs- 
down. So, you have all 
of this training data in 
the form of preferences. 
Remember that the goal 
is to use that data to 
predict ratings. 

Bipartite Graph 



User 1 

User 2 

Suppose that a user 

enjoys Braveheart. What Movie 3 T "1 User 3 

can you recommend? 

You have a slew of data 
on this user, and on other 

users and movies. One approach is to find the user whose opinions 
are closest to this particular user. What did a different person like? 
Recommend that movie to the Braveheart user. If you are trying to 
predict this user’s rating, what rating did the other user give? 


• But how do you figure out who in the data is most like this user? 
There are a few ways to do this. One way is to simply treat a row 
of data as a point. So, if you have two data entries per row, then 
you might have the point (1, 5) and (4, 5). You simply compute 
the distance between these points as if they were points in two 
dimensions. If you have five entries per row, then you find the 
distance between the points in five dimensions. 

• Another way to measure distance is known as Jaccard similarity. 
This equals the number of preferences in common divided by the 
total number of things. This method captures the fact that two users 
have similar things that they like and dislike. 

• These two distance measures are different. So, you’d want to think 
carefully about what they both mean in terms of the question you 
are asking. For example, Jaccard similarity only considers when 
users rate movies exactly the same. Any other ratings are ignored, 
whether they are close or not. 

• Measuring distance like this can have double-counting problems 
that lead to overfitting and bad performance. To help with this, we 
need to reduce what is called the dimension of the problem. We 
need to get rid of such redundancies. 

• When the Netflix competition was announced, initial work quickly 
led to improvement over the existing recommendation system. It 
didn’t reach the magic 10 percent, but the strides were impressive 
nonetheless. The key was using linear algebra, specifically a 
technique called the singular value decomposition (SVD). 

• If this algorithm is so straightforward and creates good results, 
why did the Netflix Prize take time? Had the competition been 
to improve the algorithm by 5 percent, it would have been over 
quickly. But it wasn’t. Those last percentage points took more work, 
because not everything is easily predictable. For example, there’s 
the issue of time: How you rate movies can depend on when you 
rate them. Furthermore, some movies are simply difficult to predict. 


Lecture 19: Data Compression and Recommendation Systems 

The Winner of the Netflix Prize 

• The Netflix Prize took years of work. As such, it is rather amazing 
that the first-place winner crossed that digital finish line of 10 
percent mere minutes ahead of the competitors. The team was 
BellKor’s Pragmatic Chaos, a team of computer scientists, electrical 
engineers, and statisticians. 

• Interestingly, the first-place winner and the second-place winner, 
the Ensemble, were amalgamations of teams that started off 
competing separately for the million-dollar prize. It’s when separate 
teams joined forces with other teams that the final leap beyond the 
10 percent was made. It was by combining teams and algorithms 
into more complex algorithms that those final advances were made. 

• The ideas that were further away from the mainstream proved the 
most helpful at making final improvements that won the prize. For 
example, what about the number of movies rated on a given day? 
This information didn’t predict much on its own. But movies rate 
differently on the day they are seen compared to movies reviewed 
long after viewing. And it turned out that how many movies were 
reviewed at once could be used as a proxy for how long it had 
been since a given viewer had seen a movie. In a sense, the prize¬ 
winning algorithm was a meta-algorithm that combined weights for 
a variety of simpler algorithms. 

Other Recommendation Systems 

• Recommendation systems appear all over the Internet. For example, 
Amazon recommends movies and books based on the one you are 
looking at. A different kind of example is Pandora, which has over 
70 million active listeners each month. Pandora’s success is rooted 
in an idea that was a commercial failure: the Music Genome Project. 

• Pandora was launched in 2000 by Tim Westergren and a small 
team of entrepreneurs. They wanted to create a database of musical 
characteristics for a given song in order to identify other songs with 
similar qualities. Instead of starting with listener ratings, they built 
up from data about the music itself. 


• How do you find songs that have similar attributes? Computer 
algorithms can do this, but the Music Genome Project team 
believed that such identification required a human touch. So, they 
hired musicians, who knew music theory, for example, to listen 
to each song. Then, they broke the song down by as many as 450 
predetermined attributes, giving each a numerical value. 

• They didn’t get much response to license the music recommendation 
data. So, in 2005, Tim Westergren cofounded Pandora. It still uses 
Music Genome Project data to generate the custom playlists for 
its users. And it still uses people to listen to and evaluate music. 
This is very different from what happened to in-house reviewers at 
Amazon, who were dropped very early in Amazon’s history. 

• There is another human factor in the process: the user’s interaction 
with the program. You can skip a song to hear a new one. Or, if 
you like a selection, click a thumbs-up. If you don’t, click a 

• Pandora tracks users’ interactions. Thumbs-up, thumbs-down, or 
skipping a song immediately affects what is played as the next 
song. But these things don’t all affect what is played next to the 
same degree. Skipping a song carries less weight than a thumbs- 
down. Skipping a song doesn’t have as clear of a meaning. You 
might skip a song because you’ve heard it too much or because 
you simply aren’t in the mood at the moment. But it also depends 
on how often you use a feature. If you rarely skip and have been 
using Pandora for some time and then do skip, that says a lot about 
that interaction. 

• Yet another mechanism is to use the wisdom of the crowd to 
filter and evaluate material. This is used by Reddit, which is a 
social news and entertaimnent web site. On this site, registered 
users submit content in the form of links or text posts. Users 
then vote submissions “up” or “down” to rank the post and 
determine its position on the site’s pages. In 2013, there were over 
100,000 subscribers. 


Lecture 19: Data Compression and Recommendation Systems 

• This type of site uses what is called collaborative filtering, it filters 
large amounts of information by spreading the process of filtering 
among a large group of people. Rather than having one main editor 
or group of editors, like a newspaper or magazine might have, the 
collaboratively filtered social web has its entire set of subscribers as 
editors, which also encourages participation. 

• There are two basic principles involved in collaborative filtering. 
First, there is the wisdom of crowds and the law of large numbers. 
According to this, as communities grow, they make better decisions. 
In fact, this same idea is behind YouTube. People submit videos, 
and the wisdom of the crowd enables the best videos to bubble to 
the top. 

• The second principle of collaborative filtering depends on the 
community being large enough, with enough data on individual 
participants and on how the individual participants collaborate or 
correlate with each other. The second principle suggests that we 
can make predictions about what these users will like in the future 
based on what their tastes have been in the past. That is, we can 
include collaboration when we create recommendations. 

Suggested Reading 

Elden, Matrix Methods in Data Mining and Pattern Recognition. 

Takahashi, Inoue, and Ltd. Trend-Pro Co, The Manga Guide to Lineal- 


1. From Amazon, to Pandora, to Netffix, to shopping sites, 
recommendations occur frequently. Think about what data might be 
leading to a recommendation that you see. When a recommendation is 
wrong, can you offer more data that might “recalibrate” the data? For 
example, you can make sure to rate songs or movies that you don’t like, 
and then try to observe a change in subsequent recommendations. 


2 . When you make a recommendation, can you discern how you are 
making it? What factors do you consider most heavily? Does it make 
a difference what you are recommending or to whom? As a data 
analyst, could you automate such a process to possibly make broad 
recommendations like you see online? 


Lecture 20: Decision Trees—Jump-Start an Analysis 

Decision Trees—Jump-Start an Analysis 

Lecture 20 

D ecision trees are one of the most transparent and powerful techniques 
in all of data analytics. In this lecture, you will learn how decision 
trees can help you analyze many kinds of variables. Sometimes, 
we create a decision tree, and we are done with our analysis; other times, 
decision trees are the first tool that paves the way for other methods. Either 
way, decision trees can carve quickly through your data, offering insight and 
possibly predictions about the future. 

Decision Trees 

• Let’s start with decision trees involving probability, using a medical 
example from the text Stats: Modeling the World by David Bock, 
Paul Velleman, and Richard De Veaux. There are two studies. The 
first is a study by the Harvard School of Public Health called “Binge 
Drinking on America’s College Campuses.” They found that 44 
percent of college students engage in binge drinking, 37 percent 
drink moderately, and 19 percent abstain entirely. 

• A second study appeared in the American Journal of Health 
Behavior, and it reported that among binge drinkers between the 
ages of 21 and 35, 17 percent were involved in alcohol-related auto 
accidents, while 9 percent of non-binge drinkers in the same age 
group were involved in such accidents. 

• Ignoring the fact that college students are often a bit younger than 
the 21 to 35 year olds of the second study, can we then combine 
the results of these two studies? That would let us determine 
the probability of a randomly selected college student being a 
binge drinker and in an alcohol-related car accident. A decision 
tree diagram makes it easy to combine two studies and answer 
such questions. 


Binge Drinking and 
Alcohol-Related Car Accidents 


binge and accident 

binge and none 

moderate and accident 

moderate and none 

• We’re interested in college students, so the first step is to branch 
out according to college drinking habits. So, we have a branch 
representing the 44 percent likelihood of a college student being 
a binge drinker, 37 percent likelihood of being a moderate drinker, 
and 19 percent likelihood of abstaining. 

• After we have visualized the results of the first study (about college 
students), we fold in the second study (about adult binge drinking). 
First, we have that 17 percent of the binge drinkers were involved in 
alcohol-related auto accidents. So, we add this to the branch related 
to binge drinking. Then, we also know that 9 percent of non-binge 
drinkers were involved in alcohol-related auto accidents. 

• We are interested in the probability of a randomly selected student 
being a binge drinker who had been in an alcohol-related accident. 
We find this by first following the branch for binge drinking and then 
the branch for an accident. We can multiply the two probabilities 
along each path. So, we find 0.44 times 0.17, which equals 0.075. 
In this way, we can fill out the entire tree. 


Lecture 20: Decision Trees—Jump-Start an Analysis 

• We can also use the tree to find answers not already displayed 
on the tree. For example, if someone is a drinker involved in an 
alcohol-related accident, what’s the probability that the person is a 
binge drinker? 

• To find this probability, we are interested in a ratio involving the 
top branch (a binge drinker who was involved in an accident, 
which is 0.075), and we divide that by the sum of both the branches 
involving an accident, which is 0.075 + 0.033, or 0.108. So, we find 
this probability as 0.075/0.108 = 0.694, or about 69 percent. 

• We started with two entirely separate studies, but we combined 
their data to find a clear result: The chance that a student who has 
an alcohol-related car accident is a binge drinker is more than 69 
percent. Results like this might not be obvious from the two studies, 
but the results become very clear with tree diagrams. 

• We combined the data of two studies to answer our own question. 
But you can, of course, also collect the data yourself. Decision trees 
can enable us to combine results to answer new questions or use 
probabilities from a large data set to study particular cases. 

• Decisions trees have been a powerful research technique in medical 
research on heart disease, which is the leading cause of death in 
the world. A nice attribute of decision trees is that they produce 
a questionnaire that a doctor, or patient, can ask. They produce 
essentially an if-then-else type of structure: If this, then ask this, 
or else ask this other question. Furthermore, at each step of the 
process, we have a probability of someone having heart disease. 

• There are nice but expensive programs that can help you create 
decision trees, but there are also inexpensive add-ins to spreadsheets 
like Excel. JMP software is not cheap, but you click a button and 
the data splits. You feed the program a table of data where one 
column is what you are trying to predict, and the other columns 
contain what might be predictive of that outcome. Then, you click 
to see what this might find. 


• Like any technique, this may not work, because it is splitting 
one variable at a time and thereby missing something when two 
variables must happen concurrently. Still, it can be interesting and 
is frankly a fun way to explore data. 

Classification Methods 

• Decision trees are part of the larger field of classification. We use 
data to classify. Classification methods help detect a spam e-mail 
message from its header and content. Galaxies can be classified 
based on their shape as spiral or elliptical, and then split further. 
Banks can take data to determine if they believe someone should 
be given a home loan. In this case, you are predicting if someone 
might default on a loan. The resulting probability indicates if 
someone could be seen as safe or risky for a loan. 

• Who visits each web page offers another example. When you visit 
a web page, a digital trail of sorts is left. Not a lot of information is 
available, but some is. For example, a data analyst would be able 
to see that you use the Chrome browser to connect with your Mac 
from a particular IP address. 

• You can also know if a person visited a page by clicking a link 
on another web page. If not, that individual directly inputted the 
web page for the visit. This can be analyzed to determine the total 
number of visitors for a web page, along with how many visitors 
came from .edu, .com, and .gov sites. You can also tell what time 
and which day of the week people visit. 

• We can use such a log to determine when we have a person visiting 
versus when it is a web robot. Web robots, often called web crawlers, 
automatically move through the web, retrieving information about 
web pages. For example, search engines use crawlers to see which 
web pages link to each other and even some or all of the textual 
content on the page. 


Lecture 20: Decision Trees—Jump-Start an Analysis 

• Suppose that a business would like to know if people repeatedly 
visit a web site, and if so, which products they view. If someone 
does visit the same product more than once, do rebates or free 
shipping aid in the customer making a purchase? To analyze such 
things, we first must remove web robot movement through the 
web pages. 

• Rather than analyze such data immediately, we can combine the 
data in order to better analyze it. That is, we use the original data 
to construct a number of attributes not directly in the original data. 
In short, we are taking the data and essentially making a new data 
set that we can then analyze to gain insight on the original data. The 
resulting decision tree allows us to clear a lot of the noise out of our 
data and do a more meaningful analysis. 

Advantages and Limitations of Decision Trees 

• Decision trees have some important advantages. First, they are 
simple to understand and interpret. You don’t need to know about 
data mining, or even much about the data itself, to understand how 
to use the results. 

• Second, decision trees require little data preparation, apart from 
maybe combining pieces of data. Other methods may perform 
better, but preparing the data can take a much longer time. 

• Finally, decision trees are often called a white-box method. We 
are able to take the residts and see from the data why each split is 
made. Other methods, by contrast, give a black-box result, meaning 
that the result is often harder to see or explain. 

• Decision trees do have limitations. First, decision trees aren’t 
necessarily performing the best split. For example, a method might 
only make one split at a time. If we want an analysis that could 
be varied by changing the splits, we could use other tools, such 
as support vector machines—a model for machine learning that is 
basically a more nuanced way to do splits. 


• Regardless of the underlying method, we can’t consider every 
possible split, especially for larger data sets with more attributes. 
So, we choose some way of splitting and do the best we can. Just 
keep in mind that the split may not be the best. 

• On the other hand, if you split too many times, you may have great 
results for the data you are looking at, but it may not work well 
for future data. This is called overfitting. Another problem with 
splitting too much is that the rules become quite complicated. 
Statistically, you may have a good descriptor of who to give a loan 
to, but it may become more difficult for a loan officer to implement. 

• There could be subtle interdependencies in the data that a decision 
tree will not capture. Even so, it can bring us down to a manageable 
number of variables. Then, we can turn to regression and neural 
networks to refine the analysis. If you include too many variables 
in regression or neural networks, the data gets memorized. As such, 
you perfectly describe the data you have—that is overfit—but then 
you can’t predict future behavior, except in the rare case that it 
already matches (perfectly) with past events. 

• Decision trees are a powerful technique not only for decisions 
we make, but also for understanding all kinds of factors and 
probabilities contributing to a set of outcomes. Always ask yourself 
whether you can use a decision tree whenever you look at data. 
With decision trees, you create a huge sieve in the data deluge, 
keeping a lot of the good stuff and getting rid of a lot of the noise. 

Suggested Reading 

Bock, Velleman, and De Veaux, Stats. 

Conway and White, Machine Learning for Hackers. 


Lecture 20: Decision Trees—Jump-Start an Analysis 


1. If you have a data set that you want to use, think about whether you run 
a decision tree on it. Again, it is one of the best initial tools, along with 
graphing, to use on data. 

2 . A key to decision trees is the output variable. What are you predicting? 
Is it one thing? Even if you don’t have data, simply looking at life for 
things that could be analyzed with tools you learn if you did have the 
data is increasing your ability to think like a data analyst. 


Clustering—The Many Ways to Create Groups 

Lecture 21 

C lustering is a powerful family of analytics for sorting data into 
groups—what we call clusters. There is more than one way to sort 
data into clusters, and which clustering method you use depends in 
part on your data. In fact, the choice of method can also affect the results you 
get—all clustering is not the same. Clustering is a widespread technique in 
data analysis. From political science, to medicine, to sports, to economics, 
clustering can be a tool to find connections and similarities in large data sets 
that otherwise can go unnoticed. 


• We saw one type of clustering in the last lecture, where we used 
decision trees to carve the data into groups. Decision trees are great 
when you have a directed flow of all your data toward a single 
target variable. But often, we want to look for groups where there 
is no carving of the data based on a single master variable. In those 
cases, clustering techniques are more appropriate. 

• Fighting crime through predictions might sound like science fiction, 
but in fact, the analytics leading to this sort of police work started in 
the mid-1990s. The idea began with researchers Lawrence Sherman 
and David Weisburd, who developed a concept of clustering known 
as hot spots. 

• They defined hot spots as “small places in which the occurrence 
of crime is so frequent that it is highly predictable, at least 
over a 1-year period.” According to their research, crime is 
approximately six times more concentrated among places than it is 
among individuals. 


Lecture 21: Clustering—The Many Ways to Create Groups 

• For example, one can find hot spots for robberies in the Bronx, 
which can help direct police to that area, for that type of crime. 
Even better, doubling or tripling the frequency of police patrols 
in these crime hot spots was found to reduce street crime rates 
by two-thirds. 

• This idea of grouping items has many applications. In education, 
clustering can help identify schools or students with similar 
properties. In geology, clustering can help evaluate reservoir 
properties for petroleum. 

• Market researchers group data from surveys and test panels. 
They group consumers into market segments. This can help find 
previously unidentified customers, develop new products, and 
select test markets. 

• Deciding how many groups to make is a common and very 
important question in clustering. Algorithms can be used to form 
groups—by math. So, we must first see if we can make sense of how 
the math grouped, and second, see if there is something unexpected 
that the math found. There’s an inherent balance there. Clustering at 
its best can discover something surprising. However, if everything 
is surprising, it’s entirely likely that the method isn’t working well 
with that set of data. So, you look to another clustering method. 

• One way clustering methods differ is over how to calculate distance. 
Euclidean distance is measuring distance in space—for example, in 
the ry-plane. Another measure is by angle and is often called cosine 
similarity because you can measure by the cosine of the angles. 

Clustering Methods 

• Hierarchical clustering is a clustering method in which you don’t 
have to decide on the number of clusters until you’re done. This 
is a big feature of this clustering algorithm, and it’s not true of the 
others in this lecture. 


• To begin, each object is assigned to its own cluster. Then, we find 
the distance between every pair of clusters. We merge the two 
clusters with the shortest distance. Then, with this new group of 
clusters, we find the distance between every pair of clusters. We 
again merge the two closest clusters. We repeat until everything 
is in one single cluster. Then, we look at the process visually and 
make decisions on the final clustering that we may use. 

• One part of this process is how to measure the distance between 
clusters. One way is to measure it as the shortest distance between 
a pair of points in each cluster. This method is based on the core 
idea of objects being more related to nearby objects than to 
objects that are farther away. These algorithms do not provide a 
single partitioning of the data set, but instead provide an extensive 
hierarchy of clusters that merge with each other at certain distances. 

• Hierarchical clustering is more flexible than some methods, but the 
results are not always as clear. This method provides a multilevel 
interpretation. You can easily zoom in and out to find something 
like subgenres, and you don’t have to the number of clusters in 
advance. But you do have to specify how to measure the distance 
between clusters. It could be the distance between centers of your 
clusters, but it also might be the two closest points between clusters 
or even the two points farthest from each other in the two groups. 

• Hierarchical clustering methods can potentially help with diagnosis. 
When a new patient with an unknown classification arrives, their 
data can be compared with the data from the existing classified 
clusters, and a classification for this new patient can be determined. 

• Another common clustering method is called k-means clustering. 
This method is useful for point-wise data with distances. It creates 
what can be thought of as globs of data, where you choose the 
number of globs in advance. The k-means algorithm finds k cluster 
centers and assigns the objects to the nearest cluster center, such 
that the squared distances from the cluster are minimized. 


Lecture 21: Clustering—The Many Ways to Create Groups 

• First, you must choose the number of clusters at the outset. This is 
generally associated with the variable k, which is why the method is 
called k-means. Choose k center points, or centroids, for the clusters 
you are about to fonn. You can pick k points from the set of points 
you are about to cluster, or even k random points to begin. 

• Assign each element of the data set to the nearest centroid. These are 
the clusters. Next, calculate new centroids: These are the average, 
or mean, of the data points in each cluster. Again, assign each 
element of the data set to the nearest centroid. Once every point is 
assigned, you have k clusters. Continue calculating new centroids 
and then new corresponding clusters until the clusters don’t change. 

• One big decision that has to be made is the initial centroids. It 
turns out that different initial choices can lead to different clusters. 
That can be a downside to k-means, but it is easy to run and 
quick to check. That’s a huge benefit. One use of k-means is with 
downsampling images, which is usually done to reduce the memory 
of an image. 

• Another method is spectral clustering. If you have data that’s a 
graph (with vertices and edges), then you should use spectral 
clustering, which also looks for globs, but now in graphs. Spectral 
clustering uses eigenvectors from linear algebra to get more of the 
connections into a submatrix and not as many outside of it. A nice 
attribute of spectral clustering is that you get a unique solution, and 
it even finds an optimal solution. 

• With globs defined using k-means, you have to pick the number 
of means in advance. With spectral graphs, you automatically get 
powers of two, which may not be what you want. You can’t tell it to 
find seven, for example. 


• These are only some of the most common methods; many more 
exist. Regardless of which method you use, clustering is used as 
an exploratory method. Clustering is about grouping items, so they 
lose their individuality. A particular description might not apply 
entirely to any one person you know, but parts of it might describe 
people you know. We look for a method that groups each person 
more accurately than alternatives. 

• It is important to keep in mind that data analysis or math does the 
grouping. With the exception of decision trees, we generally can’t 
immediately see why the groups turn out the way they do. So, once 
we get the results, we might need to collaborate with an expert who 
knows the data or application. 

Using Software to Cluster 

• Many times, data analysts take tables of data and use software 
to cluster. Many packages come with k-means and hierarchical 
clustering methods. There are many more. However, inherent to 
this work is deciding what to cluster. 

• A few things can go wrong: The distance measure makes no sense, 
or the clustering itself doesn’t fit the application. The easiest way 
to see why distance measures change is to think of driving. Do you 
want to know the distance between locations “as the crow flies” or 
how far they are given the roads you would have to drive on? Those 
aren’t necessarily the same, and which one you want probably 
depends on why are you asking. 

• Clustering algorithms generally have a style of problem they do well 
on. You start with the data, think about how to measure distance or 
similarity, and then choose a clustering algorithm. Clustering often 
starts by telling you pretty much what you’d expect. You may not 
have needed math to tell you much of what you’re seeing, but then 
comes something unexpected. And with that unexpected result can 
come the insight. 


Lecture 21: Clustering—The Many Ways to Create Groups 

• You look at the surprising data carefully. If possible, you verify 
the results, and you look at supporting data to ground your insight. 
And those surprising results can be where you learn something 
about that item—but often about the overall data, too. In the end, 
clustering can be most useful when it produces something that you 
in no way expected. 

Suggested Reading 

Foreman, Data Smart. 

Gan, Ma, and Wu, Data Clustering. 


1. Suppose that you have a data set of ratings where the rows are users and 
the columns are movies. Then, taking each row as a data point gives you 
a vector of user ratings. So, clustering the rows gives you similar users 
and could help with recommendation. Clustering over the columns can 
identify mathematical genres of movies. 

2 . Just like with decision trees, it can be fun and hone your ability to think 
like a data analyst if you look for aspects of life you would cluster if you 
had the data, regardless of whether you do. What would you cluster? 
What algorithm would you choose? What might you find? 

3. If you find yourself grouping people or objects (like movies) together, 
what attribute are you using to cluster? Noticing these things in your life 
can help you think about how to automate it in your data analysis when 
the data is available. 


Degrees of Separation and Social Networks 

Lecture 22 

W e definitely live in a connected world. E-mail, mobile phones, 
social media, and video chatting all enable unprecedented 
connections. But how do we analyze just how connected we are? 
This is the area of networks—what are often called social networks, even 
when there is nothing obviously social about the network. Networks, social 
or not, offer a richer, deeper dive into the relations among points in your data 
set. You can and should look to networks whenever you have a relationship 
between objects in a system, because any set of relationships can be modeled 
as a network. 

Degrees of Separation 

• One of the more famous ideas of social networks is degrees of 
separation. Part of the popularity of this idea comes from a 1993 
movie, and 1990 play, called Six Degrees of Separation. There are 
over 7 billion people on this planet, and the concept of six degrees 
of separation is that you can pick anyone on the planet, and there 
exists a path of acquaintances from that person to you (or anyone 
else), in particular, that path only consists of six people. 

• in Europe, the concept of six degrees of separation started in 1929 
with Hungarian author Frigyes Karinthy, who used it in a short 
story translated as “Chains.” In the United States, this concept dates 
back to at least 1967, when social psychologist Stanley Milgram 
conducted what became known as his small-world experiment. 

• The concept was popularized even more by Jon Stewart’s Daily 
Show in the mid-1990s, when he referenced a game created by three 
Albright College students called Six Degrees of Kevin Bacon. The 
challenge there was to connect every film actor to Bacon in six cast 
lists or fewer. 


Lecture 22: Degrees of Separation and Social Networks 

• The Internet Movie Database has over 2.6 million movies and 5.3 
million names. That’s a huge data set. But even though all of these 
people make movies together, it’s still surprising how few steps it 
takes to get from one actor or actress to another. 

In finding these connections, we are working through a 
mathematical structure called a graph. In such a graph, each vertex 
is an actor, and a link or edge is drawn between two vertices when 
both people appear in a film. 

Rather than degrees of separation, 
we talk about the distance between 
two vertices as the minimum number 
of edges that connect those vertices. 
For example, there are two edges 
between Kevin Bacon and Daniel 
Day-Lewis. Then, the eccentricity 
of a vertex is the maximum graph 
distance between that vertex and any 
other in the graph. The eccentricity 
of Kevin Bacon is claimed to be six 
or less. Finally, the center of a graph 
consists of all vertices that have the 
smallest eccentricity possible. 

A game called the Six 
Degrees of Kevin Bacon 
involves connecting every 
film actor to Bacon in six 
cast lists or fewer. 

In 2008, Microsoft, after studying 
billions of electronic messages, 
computed that any two strangers 

have on average 6.6 degrees of separation. Researchers at Microsoft 
mined through 30 billion electronic conversations among 180 
million people in various countries. 

• The database covered the entire Microsoft Messenger instant¬ 
messaging network in June 2006. This was roughly half the world’s 
instant-messaging traffic at that time. Two people were acquaintances 
if they had sent one another a message. The average distance between 
people was 6.6. Some were separated by as many as 29 steps. 


© Brendan I loffman/Getty Images Entertainment/Thinkstock. 

Social Networks 

• To investigate the connectivity of the Twitter network, social 
media analytics company Sysomos Inc. examined more than 5.2 
billion Twitter friendships (the number of friend and follower 
relationships). So, a graph of that would have 5.2 billion edges. 
After an impressive amount of careful computing, they reported in 
April 2010 that there is an average of 4.67 steps between people. 

• In November 2011, Facebook announced that there are, on average, 
just 3.74 intermediate friends separating one user from another. 
There were 721 million vertices in that graph. Interestingly, for 
a while, the average eccentricity got smaller as Facebook got 
bigger: In 2008, a much smaller Facebook had an average of 4.28 
intermediate friends. 

• These types of graphs, with edges representing connections between 
the vertices, are called social networks. They aren’t necessarily 
social in context. They can be electrical power grids, telephone call 
graphs, or the spread of computer viruses. 

• It is important to note that the type of graph needed for an 
application can differ. For example, Facebook friendships go both 
ways. If someone is your friend, you are also that person’s friend. 
That’s a graph with undirected edges. Twitter is different. You 
can follow someone, but you may or may not be followed by that 
person in return. This is a directed network, where the edges point 
from one vertex to another. 

• While similar, these can be quite different in terms of the analysis 
tools available. You can easily cluster the Facebook graph with 
a very powerful technique. It’s not that you can’t for the Twitter 
graph, but the technique for undirected graphs doesn’t immediately 
port over. 

• Then, there are other layers of analysis—just in modeling a system 
as a graph. Is the existence of any connections between objects or 
vertices all you want? Is it enough to know that you are friends with 


Lecture 22: Degrees of Separation and Social Networks 

someone else? Sometimes. But maybe you’ll want to integrate the 
number of interactions you have had with that person. 

• For Facebook, this might be the number of times you have tagged 
each other in photos and left comments on each other’s pages and 
such. For Twitter, interactions would probably be retweeting and 
mentioning. The number of interactions can be included as a weight 
on an edge. 

• Is there any other information lost? Can you integrate that into a 
graph as well? Each time you switch to a slightly different graph 
structure, that generally means different algorithms for analysis. As 
such, you can’t always answer the same questions on every graph. 
So, sometimes, the issue is not only what graph do you have, but 
which graphs might reveal the information you’re wondering about. 

• Is there a way to look at directed edges as undirected? Think about 
what happens if you simply remove the arrows. Twitter has directed 
edges, and Facebook doesn’t. If you remove the directed edges 
from Twitter, you just lost the information that you might follow 
Bill Gates, but he doesn’t follow you. So, does this make sense as a 
modeling decision? Maybe, but maybe not. 

• Social networks, with the degrees of freedom, are called small 
world networks, which is a very active field of research. It’s also 
a wonderful field for beginners, because it can be quite accessible. 

Network Analysis 

• In his book Networks: An Introduction, Mark Newman notes 
that networks really look for the pattern of connections between 
components. That pattern of interactions or structure of the network 
can have a big effect on the behavior of the system, it can affect 
how quickly news spreads and can influence how we form opinions 
or even how often we might see someone we know. 


• Your Facebook friends can indicate where you are from. That’s 
important to Facebook, because then they can provide ads and 
services in your area. But only about six percent of users enter 
their address. 

• So, how does Facebook know your address? By pictures? Maybe. 
They’re working hard on face recognition. By where you post? 
They probably do use that. But what’s interesting is that they can 
already connect you to locations just by using those six percent of 
known addresses. 

• Most people are geographically close to many of their active 
friends. So, Facebook can look at your connections to people 
with known addresses. Then, they can weight the importance of 
the edges by how recently and how much you and someone have 
been active. 

• Address is not all that can be predicted. Students at MIT 
demonstrated that sexual orientation and religion could also be 
identified by Facebook, even when such preferences weren’t 
mentioned. How? Again, by looking at the links to people for whom 
such details are known. 

• One surprise from the study of networks is that, on average, your 
friends have more friends than you do. Said a bit more technically, 
the average number of friends of friends is always greater than the 
average number of friends of individuals. This comes from a 1991 
paper by Scott Feld of the State University of New York at Stony 
Brook. It offers an interesting insight on friendship. 

• Do you ever feel like your friends have more friends than you? 
They do. And the same is generally true for them, too. How can 
this be true? It seems wrong, like a paradox. In fact, it is called the 
friendship paradox. 


Lecture 22: Degrees of Separation and Social Networks 

• This has implications in social networks like Facebook and Twitter. 
On a directed network like Twitter, the people a person follows 
almost certainly have more followers than that particular follower 
has. But the same is true on a bidirectional network like Facebook. 
Either way, the reason for the apparent paradox is there: People are 
more likely to be friends with, or follow, those who are popular 
than those who are not. 

• Today, connections can be found almost everywhere: people, 
information, events, and places. This connectivity is all the more 
evident with the advent of online social media. In this lecture, we 
see how we can gain from analyzing such connections. And there 
lies a key as you move ahead and consider network analysis. What 
you need is a connection. It doesn’t need to be between two people. 
It can, but doesn’t necessarily have to be. 

• This is why network analysis has been applied to such fields as 
sociology, mathematics, computer science, economics, and physics. 
The World Wide Web is a vast network, and how you personally 
access anything on the Internet travels through a network of routers. 
Phone calls take place more efficiently thanks to advances in how 
networks of landlines and wireless transmitters are understood 
and managed. 

• Network analysis is also shedding light on the connections between 
neurons in the brain. Better understanding of this network has led to 
advances in artificial intelligence. In addition, biology and ecology 
have long looked at networks of living species. Weights for the 
edges of such networks are moving them from the realm of cartoon 
drawings to powerful tools for analysis. 

Suggested Reading 

Easley and Kleinberg, Networks, Crowds, and Markets. 
Watt, Six Degrees. 



1. One way to play the degrees-of-separation game is on Wikipedia. Think 
of a target page, such as the page for The Great Courses, on Wikipedia. 
Then, go to a random page, which is an option on Wikipedia. Then, 
click a link on that first page that you think will get you closer to your 
target page. How many times do you have to click a link to get from the 
random page to your target page? This is sometimes called Wiki golf. 
If you play with others and start at the same beginning page, you can 
compare logic. 

2. As you think about social networks, such as actors, athletes, friends, 
or colleagues, who do you think might be a center, or at least near the 
center, of that graph? Why? Play with friends. Everyone is a winner, 
because you learn to think more like a data analyst. 


Lecture 23: Challenges of Privacy and Security 

Challenges of Privacy and Security 

Lecture 23 

Y ou have learned that there is a lot of data to analyze, and a lot of 
insight can be gained from analyzing it. In this lecture, you will 
learn about data privacy and security. What data is being analyzed? 
How difficult is it to keep data secure? And what can be done to improve 
security? With so much data, we often have less privacy than we assume we 
have, and quite a lot of information can be gleaned and known from our data. 

Security Issues with Netflix 

• In 2014, familiar smartphone apps, including Google Maps 
and Facebook, were shown to reveal personal information in 
unexpected ways—not just to the companies, but also to the U.S. 
National Security Agency and the Government Communications 
Headquarters in Britain. 

• The Netflix Prize was the million-dollar contest that challenged 
experts in data analysis to use Netflix’s data to produce better 
recommendations than Netflix did. But there was also a personal 
security angle. Netflix knew that they were supplying personal data, 
so they made an effort to remove identifying information. 

• For the first Netflix Prize, they were successful. The data set 
covered about 480,000 customers. But when a second challenge 
was announced, Netflix got sued. The lawsuit claimed that Netflix 
indirectly exposed the movie preferences of its users by publishing 
user data, even though efforts had been made to remove identifying 
information and make the users anonymous. 

• The initial data was out there, so it had already been analyzed by 
academics. In the end, 50,000 contestants participated; it was the 
fact that so many people were pouring over the data that made 
privacy advocates more worried. 


• Plaintiff Paul Navarro and others sought an injunction to prevent 
Netflix from offering that follow-up challenge. Netflix wanted to 
take the recommendation challenge another step. They promised to 
include even more personal data, such as genders and zip codes, 
which could provide interesting answers to some fundamental 
questions. But the lawsuit was settled, and the sequel competition 
was cancelled. Netflix also settled a negotiation with the Federal 
Trade Commission. 

• Netflix released data having already thought about security; they 
didn’t propose to release additional data without any forethought 
about the issue. So, how could one possibly figure out someone’s 
identity in the ratings data when Netflix believed that such info had 
been removed? 

• One way is with other data. For example, two privacy researchers 
showed that comments on another site, such as the popular 
Internet Movie Database, could help triangulate the identity of an 
“anonymous” Netflix customer. The dates for posting on both sites 
were often virtually the same. This made it easy to match entries 
from one database with the other. 

• There are ways people propose that the data could have been 
masked. The technique called data masking can randomize the data, 
making it even harder to trace an entry back to any specific person. 
Such additional precautions might have allowed the second Netflix 
challenge to go forward. But the fundamental issue remains: How 
secure is secure, and when do you know? 

Security Issues with Facebook 

• When does your privacy or security actually change without you 
knowing? Clearly, it is in Facebook’s interest to keep information 
secure. But it is also in Facebook’s interest for users to share as 
much information as possible 


Lecture 23: Challenges of Privacy and Security 

• In 2007, Facebook released a new feature called Beacon, which was 
created to enhance how people share information with their friends 
on the web about things they do. Facebook benefits when users 
share more infonnation with potential monetary value, so Facebook 
was very excited with the new feature. However, problems 
were discovered. 

• A security researcher indicated that the online advertising system 
went much further than anyone had imagined in tracking people’s 
Internet activities outside the popular social networking site. Beacon 
reported back to Facebook on members’ activities on third-party 
sites that participate in Beacon, even if the users were logged off 
from Facebook—and even if they declined having their activities 
broadcast to their Facebook friends. In fact, users wouldn’t even 
know this was happening or be given the option to block it. 

• Beacon tracked certain activities of Facebook users on more than 
40 participating web sites, such as Blockbuster and Fandango. 
Then, that user’s Facebook friends were notified. There was much 
negative buzz around this emerging news. In 2009, Beacon became 
defunct. In the end, there was a 9.5-million-dollar settlement in a 
class-action lawsuit against Facebook. The money set up a not-for- 
profit group that addresses online privacy rights. 

• Both Netflix and Facebook would have been aware of privacy 
concerns about these projects. Yet their projects did not have 
privacy as a primary objective. And they did end up having 
trouble—in both cases, costing them millions. And in both the 
cases, the U.S. Federal Trade Commission also got involved, in an 
effort to establish clearer rides about privacy online. 

• Why is Facebook so eager to know so much about you? The more 
Facebook knows, the more it can produce better, more relevant 
results for you. It can show you information about those that it 
deems are most important to you, simply by knowing who is 
connected to whom. Facebook and other companies are likely to 
know more about you than you intend. 


The Security of the U.S. Government 

• Incursions by large companies, actual or potential, can seem small 
or harmless when compared with the vast and unexpected reaches 
of the U.S. National Security Agency. Revelations during 2013, by 
a former technical contractor for the NS A named Edward Snowden, 
leaked sensitive documents. A vast system of government 
monitoring and archiving from phone lines and online services 
was unveiled. 

• Snowden made it harder for the U.S. government to spy on U.S. 
citizens and other law-abiding people. This led some to say that he 
should be protected, like a corporate whistleblower. To others, he 
was a villain. He broke the law. He made it harder to block cyber 
attacks from other countries. He made it harder to catch terrorists. 

• There are two security issues here. First, the security of U.S. 
government information was breached by the leaked documents. 
Second, the revelations showed how readily the security and 
privacy of information about individual U.S. citizens could be 
breached. Calls by U.S. citizens were revealed to have been 
recorded and stored in a vast database. The U.S. government 
defended the program as court-supervised and as a powerful tool 
that has thwarted terrorist attacks and protected citizens. 

• Part of what has made the new surveillance techniques so powerful 
was the ability to analyze previously neglected data in new ways. 
For example, a 2012 study published in Nature reported that just 
four data points about the location and time of mobile phone calls 
were needed to identify a specific caller 95 percent of the time. 

• These debates underscore two very different opinions people 
can have about security, how it is implemented, and what makes 
sense. On the one hand, the security of government information 
can be essential to the defense of the nation. On the other hand, the 
security of personal information against unreasonable search can be 
an essential value of the nation. 


Lecture 23: Challenges of Privacy and Security 

• We can expect to see at least three big practical consequences 
from these disclosures. First, organizations are rethinking how to 
effectively encrypt their most sensitive data. Second, international 
organizations consider doing less business with U.S. companies, 
because the NS A has methods and even agreements to see the data 
of U.S. companies. Third, many organizations are more hesitant to 
put their data on what was the fast-moving field of cloud computing. 

• We can also expect further disclosures. NS A and other organizations 
can sometimes install and use covert radio-wave technology to 
spy on computers that are not on the Internet. Computers with 
wireless technology can be secretly accessed from increasingly 
remote distances. 

Making Data More Secure 

• Passwords are gateways to many forms of information about you. 
IT security consultant Mark Burnett collected and analyzed over 6 
million passwords, and over 91 percent of these passwords were 
from a list of just 1,000. 

• Changing passwords frequently doesn’t help much, at least not 
if the password is short or common. It is better to have a long 
password—the longer the better. Experts like Burnett also advise 
using nonstandard spellings, including capital letters in nonstandard 
ways, and using non-letters. 

• It’s a really bad idea to use the same password across more than one 
account; not all organizations have equally good security. If your 
one-and-only password gets compromised anywhere, then you’ve 
just allowed it to be compromised everywhere. 

• At many levels, we want data available. We want a rich set of 
ingredients available for our computational laboratory. But that data 
is often about us—our society, our companies, our families, even 
our most personal details. So, we want it to be secure. 


7 ] 

Creating long, complicated, unique passwords is an important step you can take 
to keep your digital information secure. 

• Let’s look at how companies and the government make their data 
more secure. First, they keep outsiders out. Smartphones are less 
secure and more vulnerable than commonly believed, so they 
are not allowed to be used in the White House Situation Room, 
for example. 

• Second, they keep inside things inside. For example, a virtual 
private network (VPN) extends a private network across a public 
network, such as the Internet. Using VPN, you can send and 
receive data across shared or public networks as if it were directly 
connected to the private network. 

• There is a lot of interest in how to increase the privacy and security 
of specific data sets. One approach is more use of cryptography. 
What works for passwords can be extended to databases, even to 


© JaysonPhotography/iStock/Thinkstock. 

Lecture 23: Challenges of Privacy and Security 

the creation of encrypted data. The technology that is used to make 
commercial web sites secure can be extended to many other kinds 
of web sites. 

• Another approach is called differential privacy. This can be 
thought of as aiming to solve the Netflix Prize problem by adding 
increments of noise to a database. Work on this has been supported 
partly by Microsoft. The idea is not so much to hide the information 
entirely, but instead to mask just enough so that a data set can be 
used freely, without revealing sensitive data. 

• Should the government have more data available to help fight crime, 
espionage, or terrorism? How much data should businesses share 
with each other or the govermnent? How much should businesses 
or government share with the rest of us? 

• Even if we make up our minds about this—even if we know what 
we want—we can be going along thinking the data is secure, but it 
may not be. We will continue to see disclosures in the news, stories 
revealing data that was suddenly found to be insecure. Do not think 
that you are doing things that won’t be noticed. If you are sharing 
digital data, it might be visible. In fact, somewhere your digital data 
almost certainly is visible, at least potentially. 

Suggested Reading 

Angwin, Dragnet Nation. 

Singer and Friedman, Cybersecurity and Cyberwar. 


1. The balance between gaining insight with data and security and privacy 
is a tension we will have, likely for some time. As news stories emerge 
about data and security, think about that balance. What part of this 
tension is at the heart of the issue? Do you agree with the decision? 
What other issues do you think might emerge? 


2. Security breaches of data inevitably emerge, from small to big 
companies. In what ways did we think we were safe? Watch the news 
and reflect on your own thinking and actions. How does this new 
information change your behavior or outlook? 


Lecture 24: Getting Analytical about the Future 

Getting Analytical about the Future 

Lecture 24 

T hroughout this course, you have discovered that data analysis allows 
us to predict the future. The fancier, more recent term for this is 
predictive analytics. In the future, data analytics will continue to 
become more powerful at cracking all sorts of problems, and you may need 
to keep trying to get a successful outcome. Hopefully, the tools of data 
analytics help you explore paths you would not otherwise take. Once you 
find something, like explorers of old, plant your stake and begin exploring 
even more. 

Predicting the Future 

• Sometimes, it’s easy to predict the future, while other predictions 
aren’t very clear. Sometimes, that’s because we have lots of 
uncertainty, even if we have lots of data. Other times, predictions 
seem impossible because we don’t see any data or patterns to 
analyze at all. 

• The same range of issues comes up when we discuss trends in 
data analysis itself. Some things are clear, some have uncertainty 
that can be reduced, and others may yield to a new approach to 
gathering data. 

• First, it is clear that analytics will change. If a company you know 
today is still relevant and strong a few years from now, it will have 
adapted and changed. The world of data analytics will, in many 
cases, pass you by if you stand still. 

• Where will data analytics go? In part, it will come from what we are 
able to dream as today’s innovations emerge around us. In fact, an 
important feature of data analytics is that the more things change, 
the more things change ! They do not just stay the same. 


• No matter where we go in this changing landscape, there are some 
fundamental principles that are unlikely to change. The following 
are four principles that we can keep in mind as we think about 
predicting the future. 

o Prediction comes from insight, not just from data. When new 
technology comes, we often think that it will make things 
better. That’s not always the case. The same is true with more 
data: It’s how we use the data, not simply its presence. More 
data, if not properly analyzed, can be a problem. More data 
can mean less insight. The goal is good predictions and more 
insight, not necessarily more data. 

o The value of prediction comes from context. Analysis is not 
just what you know. You need to situate what you know so that 
it becomes the right insight for the right problem. For example, 
the insight you glean from a data set may seem relatively 
unimportant to your context but turn out to be more important 
in a larger or different context. 

o Don’t overestimate the value of any one prediction. Results 
in data analysis are better seen as an informed opinion. We 
have the ability to read a lot into results and assume causation 
where there is simply correlation. Analytics can help improve 
our predictions, but they are not necessarily stating truth or 
absolute outcomes. 

o Don’t underestimate the ability of prediction to anticipate and 
transform the future. Don’t miss the boat. Have the courage to 
follow sound predictions and believe in the results. 

Predicting the Future of Predictive Analysis 

• Keeping these fundamental principles in mind, let’s predict 
the future of predictive analysis. First, tools evolve. Google 
adapted its algorithm to stay current. Sports teams look for the 
competitive edge with new algorithms that offer new insight—from 
biomechanics, to training, to coaching strategies that can range 


Lecture 24: Getting Analytical about the Future 

from draft picks to player lineups. So, watch for tools we’ve seen in 
this course to show up in new combinations, such as clustering plus 
ranking, simulations plus differential equations, or decision trees 
plus regression or neural networks. 

• Second, we can predict that there will be new data sets. Every 
year, new medical research emerges, and with it, often new data 
to analyze and study. News media sometimes makes their data 
available for analysis. Sports data is often available, and with the 
advent of new measuring devices, entirely new data is available— 
whether play-by-play data or new data about conditioning 
and training. 

• Third, new technologies will continue to impact data analysis. This 
can be software like SportVU technology that led to a massive 
influx of new data into basketball and soccer. However, it can also 
mean new technologies that create new questions in data analysis. 

• Fourth, data analysis itself can drive new technologies. Gordon 
Moore of Intel famously jump-started wider thinking about 
information technology with a very simple analysis. He didn’t 
have a lot of data, but just by looking at a very small data set about 
improvements in Intel microprocessors over time, he was able to 
make a powerful prediction. He uncovered a rapid and regular 
doubling of processing power, and that prediction became the 
starting point for a technology road map that has shaped the entire 
information technology industry for decades. 

• New materials may become another key source of technological 
change for newfangled devices. For example, lithium-ion 
batteries, which are part of a family of rechargeable batteries, 
have applications from cars to computers. However, a general 
road map for such innovations is not yet available. Creating a new, 
revolutionary material can be a very slow process, especially when 
compared to the rate at which other new products are conceived and 
introduced to the market. 


• Projects such as the Materials Genome Initiative announced in 2013 
are aimed at using data analytics to accelerate such innovations. By 
gathering and analyzing data about materials in new ways, the goal 
is to reduce costs and cut the development time for new materials 
in half. 

• The name of this initiative 
resembles that of the Human 
Genome Project, which set 
out to map the underlying 
structure of human genes. The 
Materials Genome Initiative, 
in a similar way, is attempting 
to gain a deeper understanding 
of how elements interact to 
form more complex materials. 
Both of these are essentially 
vast data analysis projects. 
And as more becomes 
understood, scientists and 
engineers will be able to 
create new materials. 

The analysis of genetic information 
is the way of the future. 

• The Human Genome Project itself continues to stimulate new 
analytics, not only in biological research, but also in medicine. 
Sequencing a single reference genome of 3 billion base pairs for 
human DNA was a huge first step, but research is now uncovering 
a vast amount of variation between individuals—and even between 
different cells within a single person. 

• In the most distant future, we have more non-human cells in 
our bodies than we have human cells, due to all the friendly 
bacteria. The data challenges here will be even more enormous, 
as researchers try to track all the members of the entire ecosystem 
that make up a single person. As human beings, we have so much 
genetic material in common, yet genetic data will also show that 
each of us is unique to a mind-boggling degree. 


Ryan McVay/Photodisc/Thinkstock. 

Lecture 24: Getting Analytical about the Future 

• Medicine will increasingly be a matter of managing and using 
large data files that track and accommodate more and more of the 
variation that exists among people. A genetic variant in one context 
might be detrimental, but neutral or beneficial in another context— 
and only detailed data, both in big studies and from individual 
patients, will be able to make sense of this. Traits may be expressed, 
or recessive, only in combination with other traits, making the 
combinatorics of variation even more complicated. 

• The tools we’ve seen for visualization will be enhanced in order to 
visualize such complex information, which will be a big gain for 
basic research. And personal data will become the cornerstone of 
personalized medicine. 

• Another spin-off from having individual genomes is identification 
and visualization. In fact, we had DNA testing in the late 1980s, 
long before the entire reference genome was sequenced. But with 
much more data, it’s becoming increasingly possible to predict 
one’s facial features just by looking at one’s DNA. 

• Paleontologists have already been using DNA visualizations to 
show us revised pictures of dinosaurs and our hominid relatives, 
such as the Neanderthals. And the work on current humans goes 
even further. Researchers began using data on gene markers for just 
a few prominent facial landmarks, such as the tip of the nose or the 
middle of each eyeball. 

• But more recent work already uses highly detailed data grids, with 
thousands of reference points, and these data grids are superimposed 
onto scans of 3-D images taken with stereoscopic cameras. The 
same progression toward a data reference map is taking place with 
the Materials Genome Initiative, in which scientists and engineers 
are seeking a road map that will make it easier to tune a new material 
to the exact properties needed for a particular application—and to 
do it faster and cheaper than ever before. 


• There are many, many combinations that can be tried and arranged 
at the atomic level. A huge number of them could have useful 
properties. However, most won’t. And going one by one simply 
isn’t possible. So, the Materials Genome Initiative is using 
computers to model known and unknown materials and to simulate 
their behavior. In the end, they have lots and lots of data to analyze 
in order to help find areas that deserve a more careful examination. 

• What do you see as a need around you? Where does data seem 
unruly? Where do we need better decisions? Where could you get 
access to data that might contain insight? What insights would 
be genuinely valuable? Answers to these questions could predict 
the next area of innovation. These could be the next advances, 
large or small. 

Suggested Reading 

Brabandere and Iny, Thinking in New Boxes. 
Siegel, Predictive Analytics. 


1. As innovations in data analysis emerge, consider the four principles 
discussed in this lecture. Does the innovation fit into one of the four, or 
is there possibly another category? What tools it is using? Does the tool 
relate to one we’ve learned, or is it something else, or is it new? 

2. As you read about data analytics or find data sets, what questions could 
you ask? What new questions might be possible? It’s fun to learn what 
others have done, and it’s even more fun to ask your own questions and 
make your own discoveries. 


March Mathness Appendix 

March Mathness Appendix 

This supplement is intended to guide you in creating your own personal 
bracket using either the Massey or Colley methods. In Lecture 16, you 
learned the Massey method, and a very slight change in the linear system 
allows you to also rank with the Colley method. Both methods have been 
used to rank college football teams and help place them into bowl games. 
The Massey method integrates scores into its ranking, and the Colley method 
only uses win and loss information. 

If you prefer not to create the matrix systems yourself but instead make the 
modeling decisions that give personalized brackets, using the weighting 
ideas outlined in the lecture, visit Professor Tim Chartier’s web page at 
Davidson College. Each year, he posts links to online resources that will 
allow you to rank and create brackets. 

The key to these methods is taking the season of data and creating the linear 
system. Then, you only need to solve the linear system using mathematical 
software or even Excel. This produces the ratings, and sorting the ratings in 
descending order creates a ranked list from first to last place. 

Massey Method 

Let’s denote the linear system for the Massey method as Mr = p, where Mis 
the Massey matrix and r is the ratings as a vector. The diagonal entry in row 
i of matrix M equals the total number of games that team i has played. The 
off-diagonal entries convey information about the games played between 
two teams. The element in row i and column j of M equals the product of—1 
and the number of times teams i and j have played each other. Finally, the / th 
row of p is the sum of the point differentials of all the games played by team 
i, where wins equate to a positive point differential and losses to negative 
values. For example, if team i won a game by 10 and lost a game by 8, then 
the accumulated point differential would be 10 - 8 = 2. Once the matrix is 
filled out, we replace the last row of Mby a row of Is. The last element of p 
is set to 0. 


Colley Method 

A small change to the linear system Mr = p results in the Colley method. In 
particular, you take the matrix M, prior to replacing the last row with Is. You 
simply add 2 to every diagonal element. Next, you use a different right-hand 
side. We’ll call this new system Cr = b. To form the z' th row of b, you compute 
1 + (W~L)/ 2, where the z th team won W 
games and lost L games. Note that this 
method does not include scores but only 
win and loss information. 

Rating Madness 

To illustrate both methods, the 
following example uses a fictional series 
of games between NCAA Division I 
men’s basketball teams. The records of 
the teams are represented by a graph in 
which an arrow points from the winning 
team to the losing team, and each 
edge is assigned a weight equaling the 
difference between the winning and losing scores. From the graph, you can 
see that each team plays every other team once, in a round-robin fashion. 

College of Charleston 

Furman University 

State University 


Davidson College 

Figure 1. A fictional season played 
between NCAA basketball teams. 

For the Colley method, the linear system is as follows. 

' 4 



f C> 










, implying 












V " 1 






where C, F, D, and A correspond to the ratings for Charleston, Furman, 
Davidson, and Appalachian State, respectively. So, the ranldng (from best 
to worst) is Furman, a tie for second between Charleston and Davidson, and 
finally Appalachian State. 


March Mathness Appendix 

For Figure 1 , the linear system for the Massey method is as follows. 

' 2 






'-2.125 s 










, implying 










, 1 





So, the ranking (from best to worst) is Appalachian State, Davidson, Furman, 
and Charleston. 

Personalized Brackets 

A bracket is formed from such rankings by simply assuming that a higher- 
ranked team wins. A simple adjustment to the system requires mathematical 
modeling decisions and results in personalized brackets. You simply weight 
the games differently. For example, you may decide that the recency of a 
game is predictive of performance in a tournament like March Madness. 

One way to measure this is to break the season into n equal parts and assign 
weights w. for i from 1 to n. For example, assigning n = 4 breaks the season 
into quarters. Letting w l = 0.25, w 2 = 0.6, w 3 = 1, and w 4 = 1.5 assumes 
that a team’s play increases in its predictability as the season progresses. For 
example, games in the first quarter of the season count as 0.25 of a game. So, 
it would be worth 0.25 of a win or loss for the teams involved. Similarly, in 
the last quarter, games count as 1.5 of a win or loss. As such, games differ 
in their contribution to the final ratings, increasing the potential of assigning 
a higher rating to teams that enter the tournament with stronger records in 
periods of the season that one deems predictive. 

In addition, given the underlying derivation of both the Colley and Massey 
methods, teams that win in such predictive parts of the season and do so 
against strong teams receive a greater reward in their increased rating. 
Furthermore, the change to the linear systems is minor. Now, a game is 
simply counted as the weight of the game in its contribution to the linear 
systems. Before the matrices C and M were formed with each game counting 
as 1, now it is simply the associated weight. 


So, returning to our example of breaking a season into quarters, a game 
would count as 0.6 games in the second quarter of the season. As such, 
the total number of games becomes the total number of weighted games. 
The only other difference is the right-hand side of Massey. Now, the point 
differential in a game is a weighted point differential, where the weighted 
differential for a game equals the product of the weight of the game and the 
point differential in that game. Again returning to our example, a game in the 
first quarter of the season that was won by 6 points would now be recorded 
as a 6(0.25), or 1.5, point win. 

Got Data? 

The last, and important, piece is downloading data that is processed to form 
the linear system. Professor Chartier uses The 
page changes in format occasionally, so specific places to look for content 
can change. On the homepage, you want to look for a link to “Data,” which 
may be under the section for “Information.” Once there, you want to find your 
sport of choice, such as “Basketball,” and then select “College” and “2014,” 
if that’s the year of interest. Note that this data is generally at the bottom 
of the web page below the series of links. Select your sport, and generally 
you need to click “All” to get to the schedules and scores information. You 
then want to request the data and select “lntra” games and download the 
“Matlab Games” and “Matlab Teams” data. These are simply text files that 
you save and can process without Matlab. With that, you are ready to process 
the data line by line, create your linear system, and rank the teams in your 
sport of interest. You may want to try this first for a professional sport, such 
as the National Football League or Major League Baseball. The process is 
the same, the linear systems are smaller, and the navigation of data is easier. 




Angwin, Julia. Dragnet Nation: A Quest for Privacy, Security, and Freedom 
in a World of Relentless Surveillance. New York: Henry Holt & Co., 2014. 
An award-winning investigative journalist looks at who’s watching you, 
what they know, and why it matters. 

Bari, Anasse, Mohamed Chaouchi, and Tommy Jung. Predictive Analytics 
For Dummies. Hoboken: John Wiley & Sons Inc., 2014. This introductory- 
level book teaches you how to use big data in a way that combines business 
sense, statistics, and computers in a new and intuitive way. 

Baumer, Benjamin, and Andrew Zimbalist. The Sabermetric Revolution: 
Assessing the Growth of Analytics in Baseball. Philadelphia: University of 
Pennsylvania Press, 2014. An all-star lineup recommends this book that 
discusses the past, current, and future of analytics in baseball. Read what’s 
happening and think about what you might do in baseball or another sport 
of interest. 

Berry, Michael J. A., and Gordon S. Linoff. Data Mining Techniques: For 
Marketing, Sales, and Customer Relationship Management. Indianapolis: 
Wiley Publishing, Inc., 2011. This book contains a wealth of examples of 
data analysis in marketing, sales, and customer service. Their examples 
on the dangers of overfitting are helpful in framing one’s thinking about 
this effect. 

Berry, Michael W., and Murray Browne. Understanding Search Engines: 
Mathematical Modeling and Text Retrieval. Philadelphia: Society for 
Industrial and Applied Mathematics, 2005. This book gives many more 
details into using the singular value decomposition for textual analysis. 

Bock, David E., Paul F. Velleman, and Richard D. De Veaux. Stats: Modeling 
the World. New York: Pearson, 2014. This is actually a text for high school 
students. It has great content on decision trees and the Titanic example. 


Boyd, Brian. On the Origin of Stories. Cambridge: Belknap Press, 2010. 
This book explains why we tell stories and how our minds are shaped 
to understand them. This gives a sense of why humans are so prone to 
see patterns. 

Brabandere, Luc De, and Alan lny. Thinking in New Boxes: A New Paradigm 
for Business Creativity. New York: Random House, 2013. Now that you are 
equipped with a data analyst’s toolbox, this book can help you begin to think 
broadly and innovatively. Innovation comes in various aspects of life, and 
as you’ve learned, data is a great place to gain new perspective to think far 
outside the box. 

Bradburn, Norman M., Seymour Sudman, and Brian Wansink. Asking 
Questions: The Definitive Guide to Questionnaire Design—For Market 
Research, Political Polls, and Social and Flealth Questionnaires. San 
Francisco: John Wiley & Sons, 2004. This is considered the classic guide to 
designing questionnaires. It illuminates one of the many issues in collecting 
data on what you want to be asking. 

Brenkus, John. The Perfection Point: Sport Science Predicts the Fastest 
Man, the Flighest Jump, and the Limits of Athletic Performance. New York: 
Harper Collins Publishers, 2010. This is a book that uses data and math 
modeling to predict the limits in sports. Read the book and see if you agree 
with the analysis. If not, run your own tests and create your own predictions. 

Chartier, Tim. Math Bytes: Google Bombs, Chocolate-Covered Pi, and 
Other Cool Bits in Computing. Princeton: Princeton University Press, 
2014. In this book, I show how I found my celebrity look-alike among a 
library of 16 celebrities. These types of ideas model how facial recognition 
is done. In Lecture 11, we discussed these types of ideas as a means to 
identify handwriting. 

Conway, Drew, and John Myles White. Machine Learning for Hackers. 
Sebastopol: O’Reilly Media, 2012. This book is intended for coders but 
helps the reader understand machine learning, a field in which decision trees 
lie, and their use in hands-on case studies. 



Davenport, Thomas H. Big Data at Work: Dispelling the Myths, Uncovering 
the Opportunities. Boston: Harvard Business Review Press, 2014. This book 
looks at data analytics from the business perspective, using examples from 
companies that include UPS, GE, Amazon, United Healthcare, Citigroup, 
and many others. It really helps you get the big idea of big data in business. 

deRoos, Dirk. Hadoop for Dummies. Hoboken: John Wiley & Sons, 2014. 
To really get an appreciation for Hadoop, you may want to dive into this 
book to see its flexibility and power. 

Devlin, Keith. The Math Instinct: Why You’re a Mathematical Genius 
(Along with Lobsters, Birds, Cats, and Dogs). New York: Basic Books, 
2006. Among its various topics, this book examines how we improve our 
math skills by learning from dogs, cats, and other creatures that “do math.” 

Easley, David, and Jon Kleinberg. Networks, Crowds, and Markets: 
Reasoning about a Highly Connected World. New York: Cambridge 
University Press, 2010. If you want to leam and do research in social 
networks, this is the book for you. 

Elden, Lars. Matrix Methods in Data Mining and Pattern Recognition 
(Fundamentals of Algorithms). Philadelphia: Society for Industrial and 
Applied Mathematics, 2007. This book gives an in-depth look at matrix 
methods in the field of data mining. If you want a deep dive, this is a great 
book to help you on your journey. 

Foreman, John W. Data Smart: Using Data Science to Transform 
Information into Insight. Indianapolis: John Reilly & Sons, 2014. This book 
covers far more than clustering, with a chapter also on, among other things, 
simulation. It also discusses k-means and other clustering method, such as 
spherical k-means and graph modularity. 

Gan, Guojun, Chaoqun Ma, and Jianhong Wu. Data Clustering: Theory, 
Algorithms, and Application. Philadelphia: ASA-SIAM, 2007. This is a more 
technical book, but it is one that student researchers can use when working 
in clustering. 


Gladwell, Malcolm. Outliers: The Story of Success. New York: Little, 
Brown, and Company, 2008. This book is a good reminder that an outlier can 
lead to innovation and success. There are many reasons to identify an outlier. 
As you read this book, think about what indicators might have signaled 
someone’s success as an outlier. 

-. The Tipping Point: How Little Things Can Make a Big Difference. 

Boston: Little, Brown, and Company, 2000. A well-built simulation can 
help identify how small changes can lead to big changes in events. This 
book can help you gain a perspective on this effect from a large variety of 
well-written examples. 

Gray, Chambers, and Liliana Bounegru. The Data Journalism Handbook. 
Sebastopol: O’Reilly Media, 2012. This book provides a discussion of 
getting data without having to be a computer programmer. 

Hsu, Feng-Hsiung. Behind Deep Blue: Building the Computer That Defeated 
the World Chess Champion. Princeton: Princeton University Press, 2002. 
This book tells the compelling tale of the work of humans behind making 
a computer that shocked the chess world by defeating the defending 
world champion. 

Hurwitz, Alan Nungent, Fern Halper, and Marcia Kaufman. Big Data for 
Dummies. Indianapolis: John Wiley & Sons, 2003. This book contains a 
really helpful discussion on data management and Hadoop. 

Johnson, Neil. Simply Complexity: A Clear Guide to Complexity Theory. 
London: Oneworld Publications, 2010. This book discusses complexity 
theory and connects to traffic jams, stock market crashes, and predicting 
shopping habits. 

Langville, Amy N., and Carl D. Meyer. Google's PageRank and Beyond: The 
Science of Search Engine Rankings. Princeton: Princeton University Press, 
2006. This book is great for the deeper mathematics behind search engines— 
not just Google, but a variety of approaches. It also tells the history with 
wonderful and valuable insights along the way. 



-. Who’s #1?: The Science of Rating and Ranking. Princeton: Princeton 

University Press, 2012. This is the book of ranking. It gives you the math 
and the details of numerous methods and the math behind them, which can 
help you adapt and extend them depending on your interests. 

Levitin, Anany, and Maria Levitin. Algorithmic Puzzles. Oxford: Oxford 
University Press, 2011. Do you want to learn different types of computer 
algorithms that programmers use to tackle problems? This book teaches it 
without coding. You learn through solving puzzles, some of which are even 
offered on job interviews. 

Levitt, Steven D., and Steven J. Dubner. Freakonomics: A Rogue Economist 
Explores the Hidden Side of Everything. New York: HarperCollins Publisher, 
2005. This book, along with the companion web site http://freakonomics. 
com/, model how to look at the world as a data analyst. Given Lecture 11 ’s 
topic, pay close attention to how many topics discuss regression. 

Lewis, Michael. Moneyball: The Art of Winning an Unfair Game. New York: 
W. W. Norton & Company Inc., 2004. This is the book that detailed the surge 
in data analytics with the Oakland A’s. 

Mayer-Schonberger, Viktor, and Kenneth Cukier. Big Data: A Revolution 
That Will Transform How We Live, Work, and Think. New York: Eamon 
Dolan/Houghton Mifflin Harcourt, 2013. This book can help you see the 
wide range of problems data can tackle, helping broaden your perspective as 
a data analyst. For example, which paint color is most likely to tell you that a 
used car is in good shape? 

Montfort, Nick, Patsy Baudoin, John Bell, Jeremy Douglass, Mark C. 
Marino, Michael Mateas, Casey Reas, Mark Sample, and Noah Vawter. 10 
PRINT CHR$(205.5+RND(1)); : GOTO 10. Cambridge: Massachusetts 
Institute of Technology, 2013. Starting with a single line of code, this book 
dives into creative computing. This book is collaboratively written, taking 
text that appeared in many different printed sources. It’s an example of 
textual analysis in the humanities with an ever-growing field. 


Neuwirth, Erich, and Deane Arganbright. The Active Modeler: Mathematical 
Modeling with Microsoft Excel. Belmont: Thomson/Brooks/Cole, 2004. This 
book contains a wide range of applications in math modeling and details 
how to analyze them with a spreadsheet program—in this case, Excel. 

Oliver, Dean. Basketball on Paper: Rules and Tools for Performance 
Analysis. Washington, DC: Brassey’s Inc., 2004. This book is for every 
student interested in an exceptional study that explains the winning, or 
losing, ways of a basketball team with data. 

Osborne, Jason W. Best Practices in Data Cleaning: A Complete Guide 
to Everything You Need to Do Before and After Collecting Your Data. Los 
Angeles: Sage Publications Inc., 2013. Remember, if you are going to work 
with data, you may need to spend time preparing it. This book gives easy-to- 
implement strategies to aid you. 

Paulos, John Allen. Innumeracy: Mathematical Illiteracy and Its 
Consequences. Boston: Holt McDougal, 2001. This classic book underscores 
the possible implications of not understanding numbers. If you read this 
book and think about it in our modern world of data, the points and stories 
become all the more compelling. 

Russell, Matthew A. Mining the Social Web: Data Mining Facebook, Twitter, 
Linkedln, Google+, GitHub, and More. Sebastopol: O’Reilly Media, 2014. 
Students use this book to learn to tap into social web data. 

Shapiro, Amram, Louise Firth Campbell, and Rosalind Wright. The Book of 
Odds: From Lightning Strikes to Love at First Sight, the Odds of Everyday 
Life. New York: HarperCollins Publishers, 2014. Simulations are often built 
on probabilities and odds of events occurring. This book can supply a wealth 
of such information and, if nothing else, can be a very fun read about data. 

Siegel, Eric. Predictive Analytics: The Power to Predict Who Will Click, Buy, 
Lie, or Die. Hoboken: John Wiley & Sons Inc., 2013. This book will review 
many concepts of this course, including the Netflix Prize and machine 
learning, and it also offers predictions for the future. 



Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail—But 
Some Don’t. New York: Penguin Press, 2012. This book, while not giving a 
lot of details of Silver’s work, really underscores and lays out his thinking. 
This can help you understand the mindset that led to Silver’s innovations. 
This book can also help frame your thinking about data and looking for 
insight in the noise of everyday events. 

Singer, P. W., and Allan Friedman. Cybersecurity and Cyberwar: What 
Everyone Needs to Know. New York: Oxford University Press, 2014. This 
book discusses critical issues in this field while keeping focused on the key 
questions in cyberspace and its security: how it all works, why it all matters, 
and what we can do. 

Smiciklas, Mark. The Power of Infographics: Using Pictures to 
Communicate and Connect with Your Audiences. Upper Saddle River: 
Pearson Education Inc., 2012. This book not only gives a wonderful array of 
ideas for infographics but also discusses why we are so hardwired to digest 
visual information quickly. 

Takahashi, Shin, Iroha Inoue, and Ltd. Trend-Pro Co. The Manga Guide to 
Linear Algebra. Translated by Fredrik Lindh. San Francisco: Oluumsha Ltd. 
and No Starch Press Inc., 2012. Linear algebra is a powerful tool of data 
mining. This can teach you many ideas in the field in an entertaining style. 

Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. Introduction to Data 
Mining. Boston: Pearson Addison-Wesley, 2005. This is a text, but it is a 
very complete guide to data mining. It also has very instructive and complete 
sections on data preparation. 

Tufte, Edward. The Visual Display of Quantitative Information. Cheshire: 
Graphics Press, 2001. This is a definitive resource on visually displaying 
data for many statisticians. 

Vise, David A. The Google Story: For Google’s 10' h Birthday. New York: 
Random House Inc., 2005. This book tells you even more of the tale behind 
Google’s journey, from struggling for funding in 1998 to a data analytics 
mega success story. 


Warwick, Kevin. Artificial Intelligence: The Basics. New York: Routledge, 
2012. What are the blended boundaries of robots? Can machines think? This 
book can help give you insight on artificial intelligence as a larger field and 
one that has important implications for data analysis. 

Watt, Duncan J. Six Degrees: The Science of a Connected Age. New York: 
W. W. Norton & Company Inc., 2004. This author is an expert in network 
theory and discusses a wide range of ideas in networks, from computers, 
to economies, to terrorist organizations. Learn about this field and how it is 
emerging to uncover insight. 

Winston, Wayne L. Mathletics: How Gamblers, Managers, and Sports 
Enthusiasts Use Mathematics in Baseball, Basketball, and Football. 
Princeton: Princeton University Press, 2009. This entertaining book looks 
at a wide range of sports and delves into answering questions in each sport.