Practical Data Science with R

Practical Data Science with R

by Nina Zumel, John Mount

389 pages· 2014· ISBN 9781617291562
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Summary

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Book

Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.

Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.

This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.

What's Inside

  • Data science for the business professional
  • Statistical analysis using the R language
  • Project lifecycle, from planning to delivery
  • Numerous instantly familiar use cases
  • Keys to effective data presentations

About the Authors

Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.

Table of Contents

    PART 1 INTRODUCTION TO DATA SCIENCE
  1. The data science process
  2. Loading data into R
  3. Exploring data
  4. Managing data
    5. PART 2 MODELING METHODS
  5. Choosing and evaluating models
  6. Memorization methods
  7. Linear and logistic regression
  8. Unsupervised methods
  9. Exploring advanced methods
    10. PART 3 DELIVERING RESULTS
  10. Documentation and deployment
  11. Producing effective presentations

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