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Practical Data Science with R
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Practical Data Science with R
by Nina Zumel, John Mount
Practical Data Science with R, Second Edition
Copyright
Brief Table of Contents
Table of Contents
Praise for the First Edition
Foreword
Preface
Acknowledgments
About This Book
About the Authors
About the Foreword Authors
About the Cover Illustration
Part 1. Introduction to data science
Chapter 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
Summary
Chapter 2. Starting with R and data
2.1. Starting with R
2.2. Working with data from files
2.3. Working with relational databases
Summary
Chapter 3. Exploring data
3.1. Using summary statistics to spot problems
3.2. Spotting problems using graphics and visualization
Summary
Chapter 4. Managing data
4.1. Cleaning data
4.2. Data transformations
4.3. Sampling for modeling and validation
Summary
Chapter 5. Data engineering and data shaping
5.1. Data selection
5.2. Basic data transforms
5.3. Aggregating transforms
5.4. Multitable data transforms
5.5. Reshaping transforms
Summary
Part 2. Modeling methods
Chapter 6. Choosing and evaluating models
6.1. Mapping problems to machine learning tasks
6.2. Evaluating models
6.3. Local interpretable model-agnostic explanations (LIME) for explai- ining model predictions
Summary
Chapter 7. Linear and logistic regression
7.1. Using linear regression
7.2. Using logistic regression
7.3. Regularization
Summary
Chapter 8. Advanced data preparation
8.1. The purpose of the vtreat package
8.2. KDD and KDD Cup 2009
8.3. Basic data preparation for classification
8.4. Advanced data preparation for classification
8.5. Preparing data for regression modeling
8.6. Mastering the vtreat package
Summary
Chapter 9. Unsupervised methods
9.1. Cluster analysis
9.2. Association rules
Summary
Chapter 10. Exploring advanced methods
10.1. Tree-based methods
10.2. Using generalized additive models (GAMs) to learn non-monotone relationships
10.3. Solving “inseparable” problems using support vector machines
Summary
Part 3. Working in the real world
Chapter 11. Documentation and deployment
11.1. Predicting buzz
11.2. Using R markdown to produce milestone documentation
11.3. Using comments and version control for running documentation
11.4. Deploying models
Summary
Chapter 12. Producing effective presentations
12.1. Presenting your results to the project sponsor
12.2. Presenting your model to end users
12.3. Presenting your work to other data scientists
Summary
Appendix A. Starting with R and other tools
A.1. Installing the tools
A.2. Starting with R
A.3. Using databases with R
A.4. The takeaway
Appendix B. Important statistical concepts
B.1. Distributions
B.2. Statistical theory
B.3. Examples of the statistical view of data
B.4. The takeaway
Appendix C. Bibliography
Practical Data Science with R
Index
List of Figures
List of Tables
List of Listings
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Appendix C. Bibliography
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Index
Practical Data Science with R
The lifecycle of a data science project: loops within loops
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