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

• R in Action: Data Analysis and Graphics with R
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
• R for Everyone: Advanced Analytics and Graphics (2nd Edition) (Addison-Wesley Data & Analytics Series)
• Applied Predictive Modeling
• Practical Statistics for Data Scientists: 50 Essential Concepts
• Deep Learning with R
• R Cookbook (O'Reilly Cookbooks)
• Python Data Science Handbook: Essential Tools for Working with Data
• Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition