Practical Data Science with R
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A unique and important addition to any data scientist’s library.
NEWER EDITION AVAILABLE IN MEAP
Practical Data Science with R, Second Edition is now available in the Manning Early Access Program. An eBook of this older edition is included at no additional cost when you buy the revised edition!
You may still purchase Practical Data Science with R (First Edition) using the Buy options on this page.
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.
Table of Contents takes you straight to the bookdetailed table of contents
foreword
preface
acknowledgments
about this book
about the cover illustration
bibliography
Part 1 Introduction to data science
1. The data science process
1.1. The roles in a data science project
1.2. Stages of a data science project
1.3. Setting expectations
1.4. Summary
2. Loading data into R
2.1. Working with data from files
2.2. Working with relational databases
2.3. Summary
3. Exploring data
3.1. Using summary statistics to spot problems
3.2. Spotting problems using graphics and visualization
3.3. Summary
4. Managing data
4.1. Cleaning data
4.2. Sampling for modeling and validation
4.3. Summary
Part 2 Modeling methods
5. Choosing and evaluating models
5.1. Mapping problems to machine learning tasks
5.2. Evaluating models
5.3. Validating models
5.4. Summary
6. Memorization methods
6.1. KDD and KDD Cup 2009
6.2. Building single-variable models
6.3. Building models using many variables
6.4. Summary
7. Linear and logistic regression
7.1. Using linear regression
7.2. Using logistic regression
7.3. Summary
8. Unsupervised methods
8.1. Cluster analysis
8.2. Association rules
8.3. Summary
9. Exploring advanced methods
9.1. Using bagging and random forests to reduce training variance
9.2. Using generalized additive models (GAMs) to learn non-monotone relationships
9.3. Using kernel methods to increase data separation
9.4. Using SVMs to model complicated decision boundaries
9.5. Summary
Part 3 Delivering results
10. Documentation and deployment
10.1. The buzz dataset
10.2. Using knitr to produce milestone documentation
10.3. Using comments and version control for running documentation
10.4. Deploying models
10.5. Summary
11. Producing effective presentations
11.1. Presenting your results to the project sponsor
11.2. Presenting your model to end users
11.3. Presenting your work to other data scientists
11.4. Summary
Appendix A: Working with R and other tools
Appendix B: Important statistical concepts
Appendix C: More tools and ideas worth exploring
index
About the Technology
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.
About the book
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.
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 reader
This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.
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.
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