Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking - Foster Provost

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Data Science for Business

What You Need to Know About Data Mining and Data-Analytic Thinking

By: Foster Provost

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Paperback | 16 August 2013

At a Glance

Paperback
408 Pages

Dimensions(cm)
24 x 9.5 x 2

Paperback

RRP $95.00

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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.

Book features :

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 a Professor and NEC Faculty Fellow at the NYU Stern School of Business, where he has taught data science to MBAs for 15 years. His research and teaching focus on data science, machine learning, business analytics, (social) network data, and crowd-sourcing for data analytics.

Tom Fawcett has a Ph.D. in machine learning from UMass-Amherst and has worked in industrial research (GTE Laboratories, NYNEX/Verizon Labs, HP Labs, etc.). He has served as action editor of the Machine Learning journal, before which he was an editorial board member.

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