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Practical Data Science with R

Amazon.com Price:  $33.58 (as of 22/04/2019 21:29 PST- Details)

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Description

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 can face as you collect, curate, and analyze the data the most important to the success of your business. You can apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision strengthen.

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 an increasing number of collecting, curating, analyzing, and reporting on the most important business data. The R language and its associated tools provide a straightforward way to tackle day by day data science tasks without numerous academic theory or advanced mathematics.

Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to on a regular basis business situations. The use of examples from marketing, business intelligence, and decision strengthen, 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 every other scripting language is assumed.

What’s Inside

  • Data science for the business professional
  • Statistical analysis The use of the R language
  • Project lifecycle, from planning to delivery
  • Numerous in an instant 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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