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

Provost, Foster; Fawcett, Tom

ISBN 10: 1449361323 / ISBN 13: 9781449361327

Published by O'Reilly Media, 2013

About this Item

**VERY GOOD*** Contains one or two pages of minor writing or highlights, and Underlines. Legible and in good shape. Minor to slightly heavy wears on cover from warehouse shelves. (Used so may not contain codes/CDs/Inserts that is included with the book.

Seller Inventory # VB2130311785

Bibliographic Details

• Title: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
• Publisher: O'Reilly Media
• Publication Date: 2013
• Language: English
• ISBN 10: 1449361323
• ISBN 13: 9781449361327
• Binding: paperback
• Condition: Very Good
• Weight: 24 ounces

About this title

Synopsis

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

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.