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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.


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.


  • 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


Part 1 Introduction to data science

Chapter 1 The data science process
- The roles in a data science project
- Stages of a data science project
- Setting expectations
- Summary
Chapter 2 Loading data into R
- Working with data from files
- Working with relational databases
- Summary
Chapter 3 Exploring data
- Using summary statistics to spot problems
- Spotting problems using graphics and visualization
- Summary
Chapter 4 Managing data
- Cleaning data
- Sampling for modeling and validation
- Summary

Part 2 Modeling methods

Chapter 5 Choosing and evaluating models
- Mapping problems to machine learning tasks
- Evaluating models
- Validating models
- Summary
Chapter 6 Memorization methods
- KDD and KDD Cup 2009
- Building single-variable models
- Building models using many variables
- Summary
Chapter 7 Linear and logistic regression
- Using linear regression
- Using logistic regression
- Summary
Chapter 8 Unsupervised methods
- Cluster analysis
- Association rules
- Summary
Chapter 9 Exploring advanced methods
- Using bagging and random forests to reduce training variance
- Using generalized additive models (GAMs) to learn non-monotone relationships
- Using kernel methods to increase data separation
- Using SVMs to model complicated decision boundaries
- Summary

Part 3 Delivering results

Chapter 10 Documentation and deployment
- The buzz dataset
- Using knitr to produce milestone documentation
- Using comments and version control for running documentation
- Deploying models
- Summary
Chapter 11 Producing effective presentations
- Presenting your results to the project sponsor
- Presenting your model to end users
- Presenting your work to other data scientists
- Summary

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


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

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