Practical Data Science with R

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
Chapter 1 The data science process
Chapter 2 Loading data into R
Chapter 3 Exploring data
Chapter 4 Managing data

Part 2 Modeling methods
Chapter 5 Choosing and evaluating models
Chapter 6 Memorization methods
Chapter 7 Linear and logistic regression
Chapter 8 Unsupervised methods
Chapter 9 Exploring advanced methods

Part 3 Delivering results
Chapter 10 Documentation and deployment
Chapter 11 Producing effective presentations

Appendix A Working with R and other tools
Appendix B Important statistical concepts
Appendix C More tools and ideas worth exploring