Data Science for Business : What you need to know about data mining and data-analytic thinking

作者：FosterProvost/TomFawcett

出版社：O'ReillyMedia

副标题：Whatyouneedtoknowaboutdatamininganddata-analyticthinking

出版年：2013-8-16

页数：408

定价：USD39.99

装帧：Paperback

ISBN：9781449361327

内容简介
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Review

"A must-read resource for anyone who is serious about embracing the opportunity of big data."

— Craig Vaughan

Global Vice President at SAP

"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

"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

"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 principles and algorithms available without the technical details of single-disciplinary books."

— Ronny Kohavi

Partner Architect at Microsoft Online Services Division

About the Author

Foster Provost is Professor and NEC Faculty Fellow at the NYU Stern School of Business where he teaches in the MBA, Business Analytics, and Data Science programs. His award-winning research is read and cited broadly. Prof. Provost has co-founded several successful companies focusing on data science for marketing.

Tom Fawcett holds a Ph.D. in machine learning and has worked in industry R&D for more than two decades for companies such as GTE Laboratories, NYNEX/Verizon Labs, and HP Labs. His published work has become standard reading in data science.

目录
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Preface

1. Intorduction: Data-Analytic Thinking

2. Business Problems and Data Science Solutions

3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation

4. Fitting a Model to Data

5. Overfitting and Its Avoidance

6. Similarity, Neighbors, and Clusters

7. Decision Analytic Thinking I: What Is a Good Model?

8. Visualizing Model Performance

9. Evidence and Probabilities

10. Representing the Mining Text

11. Decision Analytic Thinking II: Toward Analytical Engineering

12. Other Data Science Tasks and Techniques

13. Data Science and Business Strategy

14. Conclusion

A. Proposal Review Guide

B. Another Sample Proposal

Glossary

Bibliography

Index

评论 ······

Incredible book with practical example and strategical view. Simply explained how to leverage the real "Big Data" into data-driven business and being real value added.

侧重应用的一个整体workflow的东西，想看technical细节的不要看这种书，主要看能解决一些什么问题，以及学习怎么与非技术领域的人沟通合作时，学习作者怎么阐述问题。

去年粗略翻过一遍，无干货，也无甚湿货。没必要看的书。除非你想学一堆 biz 词汇去唬人。

读了前两章，学了一堆术语，有了一点点理解@@

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