Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking / Edition 1

by Foster Provost

Overview

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

Understand how data science fits in your organization--and how you can use it for competitive advantage

Treat data as a business asset that requires careful investment if you're to gain real value

Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way

Learn general concepts for actually extracting knowledge from data

Apply data science principles when interviewing data science job candidates

Product Details

ISBN-13:	2901449361326
Publisher:	O'Reilly Media, Incorporated
Publication date:	08/16/2013
Edition description:	New Edition
Pages:	413
Product dimensions:	6.00(w) x 1.25(h) x 9.00(d)

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. Former Editor-in-Chief for the journal Machine Learning, Professor 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 both on methodology (evaluating data mining results) and on applications (fraud detection and spam filtering).

Table of Contents

• Praise
• Preface
• Chapter 1: Introduction: Data-Analytic Thinking
• Chapter 2: Business Problems and Data Science Solutions
• Chapter 3: Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
• Chapter 4: Fitting a Model to Data
• Chapter 5: Overfitting and Its Avoidance
• Chapter 6: Similarity, Neighbors, and Clusters
• Chapter 7: Decision Analytic Thinking I: What Is a Good Model?
• Chapter 8: Visualizing Model Performance
• Chapter 9: Evidence and Probabilities
• Chapter 10: Representing and Mining Text
• Chapter 11: Decision Analytic Thinking II: Toward Analytical Engineering
• Chapter 12: Other Data Science Tasks and Techniques
• Chapter 13: Data Science and Business Strategy
• Chapter 14: Conclusion
• Proposal Review Guide
• Another Sample Proposal
• Glossary
• Bibliography
• Index
• Colophon