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Practical Data Science with R
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Created/Published/Taught by:
Nina Zumel
John Mount
Manning Publications

Content Found Via:
@WinVectorLLC

Free? No

Cost Range:
$40.42 - $49.99

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Note: You could win this book in the giveaway contest going on from now until 12/12/2015! See blog post for details: 3 Ways to Win

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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

  • The data science process
  • Loading data into R
  • Exploring data
  • Managing data

PART 2 MODELING METHODS

  • Choosing and evaluating models
  • Memorization methods
  • Linear and logistic regression
  • Unsupervised methods
  • Exploring advanced methods

PART 3 DELIVERING RESULTS

  • Documentation and deployment
  • Producing effective presentations

Recommended Prerequisites: Some familiarity with basic statistics, R, or another scripting language is assumed.

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