Data Science for Business: What you need to know about data mining and data-analytic thinking [NOOK Book]

Overview

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 ...

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Data Science for Business: What you need to know about data mining and data-analytic thinking

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Overview

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
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Product Details

  • ISBN-13: 9781449374280
  • Publisher: O'Reilly Media, Incorporated
  • Publication date: 7/27/2013
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 414
  • Sales rank: 174,246
  • File size: 12 MB
  • Note: This product may take a few minutes to download.

Meet 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).

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Table of Contents

Praise

Preface

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

Proposal Review Guide

Another Sample Proposal

Glossary

Bibliography

Index

Colophon

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  • Anonymous

    Posted February 9, 2014

    When trying to learn about a new field, one of the most common d

    When trying to learn about a new field, one of the most common difficulties is to find books (and other materials) that have the right "depth". All too often one ends up with either a friendly but largely useless book that oversimplifies or a heavy academic tome that, though authoritative and comprehensive, is condemned to sit gathering dust in one's shelves. "Data Science for Business" gets it just right.

    What I mean might become clearer if I point out what this book is *not*:

    - It is *not* a computer science textbook with a focus on theoretical derivations and algorithms.

    - It is *not* a "cookbook" that provides "step-by-step" guidance with little to no explanation of what one is doing.

    - It is *not* your standard "management" title on the cool tech du jour available at airport stands and meant to be read in one sitting (buzzwords, hype and overly enthusiastic statements making up for the dearth of actual content).

    Instead, it is close to being the perfect guide for the intelligent reader who -- regardless of whether s/he has a tech background -- has a sincere desire to learn how the tools and principles of data science can be used to extract meaningful information from huge datasets. Highly recommended.

    1 out of 1 people found this review helpful.

    Was this review helpful? Yes  No   Report this review
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