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Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by [Provost, Foster, Fawcett, Tom]
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Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 1st Edition, Kindle Edition

5.0 out of 5 stars 1 customer review

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Length: 408 pages Word Wise: Enabled Enhanced Typesetting: Enabled
Page Flip: Enabled Language: English

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Product description

Product 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

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).


Product details

  • Format: Kindle Edition
  • File Size: 19607 KB
  • Print Length: 414 pages
  • Simultaneous Device Usage: Unlimited
  • Publisher: O'Reilly Media; 1 edition (27 July 2013)
  • Sold by: Amazon Australia Services, Inc.
  • Language: English
  • ASIN: B00E6EQ3X4
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Enabled
  • Enhanced Typesetting: Enabled
  • Average Customer Review: 5.0 out of 5 stars 1 customer review
  • Amazon Bestsellers Rank: #13,947 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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7 March 2018
Format: PaperbackVerified Purchase

Most helpful customer reviews on Amazon.com

Amazon.com: 4.4 out of 5 stars 194 reviews
William P Ross
5.0 out of 5 starsExcellent Introductory Summary Of Data Science
4 October 2016 - Published on Amazon.com
Format: PaperbackVerified Purchase
31 people found this helpful.
Amazon Customer
5.0 out of 5 starsI thought this would be an easy argument to make during the interview process
5 February 2016 - Published on Amazon.com
Format: PaperbackVerified Purchase
46 people found this helpful.
James Benton Lackey III
2.0 out of 5 starsInstead it has some businessy sounding bits and the start and end which feel like an afterthought
11 March 2015 - Published on Amazon.com
Verified Purchase
53 people found this helpful.
amazoner
5.0 out of 5 starsThe perfect balance
10 August 2013 - Published on Amazon.com
294 people found this helpful.
Irene P
2.0 out of 5 starsGood as supplementary material, not helpful as primary text.
21 March 2018 - Published on Amazon.com
Format: PaperbackVerified Purchase
3 people found this helpful.