大数据时代的商业智慧：数据科学与业务分析

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"Data Science for Business 是一本关于大数据和数据挖掘以及数据分析思维的书籍，由2013年出版。本书被业界专家高度推荐，适合商业领域的管理者和与数据科学家合作的人阅读，旨在帮助读者理解大数据背后的科学原理和算法，而无需深入技术细节。" 在当今数字化时代，"大数据"已经成为了商业领域的重要驱动力。"Data Science for Business" 这本书深入浅出地探讨了如何利用大数据来驱动业务决策和创新。作者试图将数据挖掘和数据分析的复杂概念转化为易于商业人士理解的语言，帮助他们更好地利用数据资源。书中可能涵盖了以下几个关键知识点： 1. **大数据的定义和特征**：大数据不仅仅是指数据量大，还包括数据的多样性、速度和价值密度。理解这些特性对于有效地管理和分析大数据至关重要。 2. **数据挖掘基础**：包括数据预处理、模式识别、关联规则学习、聚类分析等，这些都是从海量数据中提取有价值信息的关键步骤。 3. **数据思维**：如何通过数据驱动的决策制定来优化业务流程，提高效率，并预测市场趋势。 4. **算法应用**：介绍了一些常用的数据分析算法，如回归分析、决策树、随机森林、支持向量机等，以及它们在商业场景中的应用。 5. **隐私和伦理问题**：在利用大数据的同时，书中可能也讨论了数据隐私保护和道德规范，这是企业进行数据实践时必须考虑的问题。 6. **案例研究**：通过真实的商业案例，展示大数据分析如何帮助企业解决实际问题，提升竞争力。 7. **工具和技术**：可能会提及Hadoop、Spark等大数据处理框架，以及Python、R等数据分析语言，让读者了解实际操作中的工具选择。 8. **团队协作**：如何在数据科学家、业务分析师和决策者之间建立有效的沟通和协作，以实现数据的最大价值。 9. **持续学习和适应性**：随着技术的快速发展，数据科学领域的知识更新迅速，书中可能强调了终身学习和适应变化的重要性。这本书对于那些希望在大数据浪潮中抓住机遇的商业领导者来说，是一份宝贵的指南，它可以帮助他们理解并运用数据科学的力量，从而在竞争激烈的市场环境中取得优势。通过阅读，读者不仅可以掌握理论知识，还能获得实战经验，提升自身的数据素养。

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

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

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

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