Data Science for Business

Date: July 2013

ISBN: 9781449361327

Pages: 408

Language: English

Publisher: O’Reilly Media

Category: Technical


Posted on 2014-05-12, by ecabuk.

Description

Book Description

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

Table of Contents
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

Appendix A. Proposal Review Guide
Appendix B. Another Sample Proposal




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