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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.

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

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