Unformatted text preview: Data Science for Business
Tom Fawcett Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo Praise
“A must-read resource for anyone who is serious about embracing the opportunity of big data.”
— Craig Vaughan Global Vice President at SAP
“This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without
thinking data. Read this book and you will understand the Science behind thinking data.”
— Ron Bekkerman Chief Data Officer at Carmel Ventures
“A great book for business managers who lead or interact with data scientists, who wish to better understand the principals and algorithms available
without the technical details of single-disciplinary books.”
— Ronny Kohavi Partner Architect at Microsoft Online Services Division
“Provost and Fawcett have distilled their mastery of both the art and science of real-world data analysis into an unrivalled introduction to the field.”
—Geoff Webb Editor-in-Chief of Data Mining and Knowledge Discovery Journal
“I would love it if everyone I had to work with had read this book.”
— Claudia Perlich Chief Scientist of M6D (Media6Degrees) and Advertising Research Foundation Innovation Award
Grand Winner (2013)
“A foundational piece in the fast developing world of Data Science. A must read for anyone interested in the Big Data revolution."
—Justin Gapper Business Unit Analytics Manager at Teledyne Scientific and Imaging
“The authors, both renowned experts in data science before it had a name, have taken a complex topic and made it accessible to all levels, but mostly
helpful to the budding data scientist. As far as I know, this is the first book of its kind—with a focus on data science concepts as applied to practical
business problems. It is liberally sprinkled with compelling real-world examples outlining familiar, accessible problems in the business world: customer
churn, targeted marking, even whiskey analytics!
The book is unique in that it does not give a cookbook of algorithms, rather it helps the reader understand the underlying concepts behind data science,
and most importantly how to approach and be successful at problem solving. Whether you are looking for a good comprehensive overview of data
science or are a budding data scientist in need of the basics, this is a must-read.”
— Chris Volinsky Director of Statistics Research at AT&T Labs and Winning Team Member for the $1 Million Netflix
“This book goes beyond data analytics 101. It’s the essential guide for those of us (all of us?) whose businesses are built on the ubiquity of data
opportunities and the new mandate for data-driven decision-making.”
—Tom Phillips CEO of Media6Degrees and Former Head of Google Search and Analytics
“Intelligent use of data has become a force powering business to new levels of competitiveness. To thrive in this data-driven ecosystem, engineers,
analysts, and managers alike must understand the options, design choices, and tradeoffs before them. With motivating examples, clear exposition, and a
breadth of details covering not only the “hows” but the “whys”, Data Science for Business is the perfect primer for those wishing to become involved in
the development and application of data-driven systems.”
—Josh Attenberg Data Science Lead at Etsy
“Data is the foundation of new waves of productivity growth, innovation, and richer customer insight. Only recently viewed broadly as a source of
competitive advantage, dealing well with data is rapidly becoming table stakes to stay in the game. The authors’ deep applied experience makes this a
must read—a window into your competitor’s strategy.”
— Alan Murray Serial Entrepreneur; Partner at Coriolis Ventures
“One of the best data mining books, which helped me think through various ideas on liquidity analysis in the FX business. The examples are excellent
and help you take a deep dive into the subject! This one is going to be on my shelf for lifetime!”
— Nidhi Kathuria Vice President of FX at Royal Bank of Scotland Special Upgrade Offer
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Data Science for Business is intended for several sorts of readers:
Business people who will be working with data scientists, managing data science–oriented projects, or investing in data
Developers who will be implementing data science solutions, and
Aspiring data scientists.
This is not a book about algorithms, nor is it a replacement for a book about algorithms. We deliberately avoided an algorithmcentered approach. We believe there is a relatively small set of fundamental concepts or principles that underlie techniques for
extracting useful knowledge from data. These concepts serve as the foundation for many well-known algorithms of data
mining. Moreover, these concepts underlie the analysis of data-centered business problems, the creation and evaluation of data
science solutions, and the evaluation of general data science strategies and proposals. Accordingly, we organized the
exposition around these general principles rather than around specific algorithms. Where necessary to describe procedural
details, we use a combination of text and diagrams, which we think are more accessible than a listing of detailed algorithmic
The book does not presume a sophisticated mathematical background. However, by its very nature the material is somewhat
technical—the goal is to impart a significant understanding of data science, not just to give a high-level overview. In general,
we have tried to minimize the mathematics and make the exposition as “conceptual” as possible.
Colleagues in industry comment that the book is invaluable for helping to align the understanding of the business,
technical/development, and data science teams. That observation is based on a small sample, so we are curious to see how
general it truly is (see Chapter 5!). Ideally, we envision a book that any data scientist would give to his collaborators from the
development or business teams, effectively saying: if you really want to design/implement top-notch data science solutions to
business problems, we all need to have a common understanding of this material.
Colleagues also tell us that the book has been quite useful in an unforeseen way: for preparing to interview data science job
candidates. The demand from business for hiring data scientists is strong and increasing. In response, more and more job
seekers are presenting themselves as data scientists. Every data science job candidate should understand the fundamentals
presented in this book. (Our industry colleagues tell us that they are surprised how many do not. We have half-seriously
discussed a follow-up pamphlet “Cliff’s Notes to Interviewing for Data Science Jobs.”) Our Conceptual Approach to Data Science
In this book we introduce a collection of the most important fundamental concepts of data science. Some of these concepts are
“headliners” for chapters, and others are introduced more naturally through the discussions (and thus they are not necessarily
labeled as fundamental concepts). The concepts span the process from envisioning the problem, to applying data science
techniques, to deploying the results to improve decision-making. The concepts also undergird a large array of business
analytics methods and techniques.
The concepts fit into three general types:
1. Concepts about how data science fits in the organization and the competitive landscape, including ways to attract,
structure, and nurture data science teams; ways for thinking about how data science leads to competitive advantage; and
tactical concepts for doing well with data science projects. 2. General ways of thinking data-analytically. These help in identifying appropriate data and consider appropriate methods.
The concepts include the data mining process as well as the collection of different high-level data mining tasks.
3. General concepts for actually extracting knowledge from data, which undergird the vast array of data science tasks and
For example, one fundamental concept is that of determining the similarity of two entities described by data. This ability forms
the basis for various specific tasks. It may be used directly to find customers similar to a given customer. It forms the core of
several prediction algorithms that estimate a target value such as the expected resouce usage of a client or the probability of a
customer to respond to an offer. It is also the basis for clustering techniques, which group entities by their shared features
without a focused objective. Similarity forms the basis of information retrieval, in which documents or webpages relevant to
a search query are retrieved. Finally, it underlies several common algorithms for recommendation. A traditional algorithmoriented book might present each of these tasks in a different chapter, under different names, with common aspects buried in
algorithm details or mathematical propositions. In this book we instead focus on the unifying concepts, presenting specific
tasks and algorithms as natural manifestations of them.
As another example, in evaluating the utility of a pattern, we see a notion of lift— how much more prevalent a pattern is than
would be expected by chance—recurring broadly across data science. It is used to evaluate very different sorts of patterns in
different contexts. Algorithms for targeting advertisements are evaluated by computing the lift one gets for the targeted
population. Lift is used to judge the weight of evidence for or against a conclusion. Lift helps determine whether a cooccurrence (an association) in data is interesting, as opposed to simply being a natural consequence of popularity.
We believe that explaining data science around such fundamental concepts not only aids the reader, it also facilitates
communication between business stakeholders and data scientists. It provides a shared vocabulary and enables both parties to
understand each other better. The shared concepts lead to deeper discussions that may uncover critical issues otherwise
missed. To the Instructor
This book has been used successfully as a textbook for a very wide variety of data science courses. Historically, the book
arose from the development of Foster’s multidisciplinary Data Science classes at the Stern School at NYU, starting in the fall
of 2005. The original class was nominally for MBA students and MSIS students, but drew students from schools across the
university. The most interesting aspect of the class was not that it appealed to MBA and MSIS students, for whom it was
designed. More interesting, it also was found to be very valuable by students with strong backgrounds in machine learning and
other technical disciplines. Part of the reason seemed to be that the focus on fundamental principles and other issues besides
algorithms was missing from their curricula.
At NYU we now use the book in support of a variety of data science–related programs: the original MBA and MSIS programs,
undergraduate business analytics, NYU/Stern’s new MS in Business Analytics program, and as the Introduction to Data
Science for NYU’s new MS in Data Science. In addition, (prior to publication) the book has been adopted by more than a
dozen other universities for programs in seven countries (and counting), in business schools, in computer science programs,
and for more general introductions to data science.
Stay tuned to the books’ websites (see below) for information on how to obtain helpful instructional material, including lecture
slides, sample homework questions and problems, example project instructions based on the frameworks from the book, exam
questions, and more to come. NO T E
We keep an up-to-date list of known adoptees on the book’s website. Click Who’s Using It at the top. Other Skills and Concepts
There are many other concepts and skills that a practical data scientist needs to know besides the fundamental principles of data science. These skills and concepts will be discussed in Chapter 1 and Chapter 2. The interested reader is encouraged to
visit the book’s website for pointers to material for learning these additional skills and concepts (for example, scripting in
Python, Unix command-line processing, datafiles, common data formats, databases and querying, big data architectures and
systems like MapReduce and Hadoop, data visualization, and other related topics). Sections and Notation
In addition to occasional footnotes, the book contains boxed “sidebars.” These are essentially extended footnotes. We reserve
these for material that we consider interesting and worthwhile, but too long for a footnote and too much of a digression for the
main text. A NO T E O N T HE STARRED, “CURVY RO AD” SECT IO NS
The occasional mathematical details are relegated to optional “starred” sections. These section titles will have asterisk prefixes, and they will include the
“curvy road” graphic you see to the left to indicate that the section contains more detailed mathematics or technical details than elsewhere. The book is
written so that these sections may be skipped without loss of continuity, although in a few places we remind readers that details appear there. Constructions in the text like (Smith and Jones, 2003) indicate a reference to an entry in the bibliography (in this case, the 2003
article or book by Smith and Jones); “Smith and Jones (2003)” is a similar reference. A single bibliography for the entire book
appears in the endmatter.
In this book we try to keep math to a minimum, and what math there is we have simplified as much as possible without
introducing confusion. For our readers with technical backgrounds, a few comments may be in order regarding our simplifying
1. We avoid Sigma (Σ) and Pi (Π) notation, commonly used in textbooks to indicate sums and products, respectively.
Instead we simply use equations with ellipses like this: 2. Statistics books are usually careful to distinguish between a value and its estimate by putting a “hat” on variables that are
estimates, so in such books you’ll typically see a true probability denoted p and its estimate denoted . In this book we
are almost always talking about estimates from data, and putting hats on everything makes equations verbose and ugly.
Everything should be assumed to be an estimate from data unless we say otherwise.
3. We simplify notation and remove extraneous variables where we believe they are clear from context. For example, when
we discuss classifiers mathematically, we are technically dealing with decision predicates over feature vectors.
Expressing this formally would lead to equations like: Instead we opt for the more readable: with the understanding that x is a vector and Age and Balance are components of it.
We have tried to be consistent with typography, reserving fixed-width typewriter fonts like sepal_width to indicate attributes
or keywords in data. For example, in the text-mining chapter, a word like 'discussing' designates a word in a document while
discuss might be the resulting token in the data.
The following typographical conventions are used in this book:
Italic Indicates new terms, URLs, email addresses, filenames, and file extensions.
Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names,
databases, data types, environment variables, statements, and keywords.
Constant width italic
Shows text that should be replaced with user-supplied values or by values determined by context. T IP
This icon signifies a tip, suggestion, or general note. WARNING
This icon indicates a warning or caution. Using Examples
In addition to being an introduction to data science, this book is intended to be useful in discussions of and day-to-day work in
the field. Answering a question by citing this book and quoting examples does not require permission. We appreciate, but do
not require, attribution. Formal attribution usually includes the title, author, publisher, and ISBN. For example: “Data Science
for Business by Foster Provost and Tom Fawcett (O’Reilly). Copyright 2013 Foster Provost and Tom Fawcett, 978-1-44936132-7.”
If you feel your use of examples falls outside fair use or the permission given above, feel free to contact us at
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Watch us on YouTube: Acknowledgments
Thanks to all the many colleagues and others who have provided invaluable feedback, criticism, suggestions, and
encouragement based on many prior draft manuscripts. At the risk of missing someone, let us thank in particular: Panos
Adamopoulos, Manuel Arriaga, Josh Attenberg, Solon Barocas, Ron Bekkerman, Josh Blumenstock, Aaron Brick, Jessica
Clark, Nitesh Chawla, Peter Devito, Vasant Dhar, Jan Ehmke, Theos Evgeniou, Justin Gapper, Tomer Geva, Daniel Gillick,
Shawndra Hill, Nidhi Kathuria, Ronny Kohavi, Marios Kokkodis, Tom Lee, David Martens, Sophie Mohin, Lauren Moores,
Alan Murray, Nick Nishimura, Balaji Padmanabhan, Jason Pan, Claudia Perlich, Gregory Piatetsky-Shapiro, Tom Phillips,
Kevin Reilly, Maytal Saar-Tsechansky, Evan Sadler, Galit Shmueli, Roger Stein, Nick Street, Kiril Tsemekhman, Craig
Vaughan, Chris Volinsky, Wally Wang, Geoff Webb, and Rong Zheng. We would also like to thank more generally the students
from Foster’s classes, Data Mining for Business Analytics, Practical Data Science, and the Data Science Research Seminar.
Questions and issues that arose when using prior drafts of this book provided substantive feedback for improving it.
Thanks to David Stillwell, Thore Graepel, and Michal Kosinski for providing the Facebook Like data for some of...
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