ESCP Europe

Item type

eBooks

Language

English

Publication date

2013

ISBN

978-1-4493-7429-7

Note

• Unlimited access

Subjects

MEDIA - COMMUNICATION

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. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll 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 organizationand how you can use it for competitive advantage Treat data as a business asset that requires careful investment if youre 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 -- 1. Introduction: 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 and 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 -- Colophon.