《数据科学为商业赋能：理解数据驱动决策的关键指南》

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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 un‐

derstand 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 in‐

troduced 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 meth‐

ods and techniques.

The concepts fit into three general types:

1. Concepts about how data science fits in the organization and the competitive land‐

scape, including ways to attract, structure, and nurture data science teams; ways for

thinking about how data science leads to competitive advantage; and tactical con‐

cepts for doing well with data science projects.

General ways of thinking data-analytically. These help in identifying appropriate

data and consider appropriate methods. The concepts include the data mining pro‐

cess as well as the collection of different high-level data mining tasks.

General concepts for actually extracting knowledge from data, which undergird the

vast array of data science tasks and their algorithms.

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 algorithm-oriented book might present

each of these tasks in a different chapter, under different names, with common aspects

xii | Preface

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1. Of course, each author has the distinct impression that he did the majority of the work on the book.

buried in algorithm details or mathematical propositions. In this book we instead focus

on the unifying concepts, presenting specific tasks and algorithms as natural manifes‐

tations 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 co-occurrence (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 prob‐

Preface | xiii

www.it-ebooks.info

lems, example project instructions based on the frameworks from the book, exam ques‐

tions, and more to come.

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 MapRe‐

duce and Hadoop, data visualization, and other related topics).

Sections and Notation

In addition to occasional footnotes, the book contains boxed “sidebars.” These are es‐

sentially 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 note on the starred, “curvy road” sections

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 sim‐

plified as much as possible without introducing confusion. For our readers with tech‐

nical backgrounds, a few comments may be in order regarding our simplifying choices.

xiv | Preface

www.it-ebooks.info

Constant width italic

Shows text that should be replaced with user-supplied values or by values deter‐

mined by context.

This icon signifies a tip, suggestion, or general note.

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

If you feel your use of examples falls outside fair use or the permission given above, feel

free to contact us at permissions@oreilly.com.

Safari® Books Online

Safari Books Online is an on-demand digital library that delivers

expert content in both book and video form from the world’s lead‐

ing authors in technology and business.

Technology professionals, software developers, web designers, and business and crea‐

tive professionals use Safari Books Online as their primary resource for research, prob‐

lem solving, learning, and certification training.

Safari Books Online offers a range of product mixes and pricing programs for organi‐

zations, government agencies, and individuals. Subscribers have access to thousands of

books, training videos, and prepublication manuscripts in one fully searchable database

from publishers like O’Reilly Media, Prentice Hall Professional, Addison-Wesley Pro‐

fessional, Microsoft Press, Sams, Que, Peachpit Press, Focal Press, Cisco Press, John

Wiley & Sons, Syngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT

Press, Apress, Manning, New Riders, McGraw-Hill, Jones & Bartlett, Course

Technology, and dozens more. For more information about Safari Books Online, please

visit us online.

xvi | Preface

www.it-ebooks.info

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