Foreword

Practical Data Science with R, Second Edition, is a hands-on guide to data science, with a focus on techniques for working with structured or tabular data, using the R language and statistical packages. The book emphasizes machine learning, but is unique in the number of chapters it devotes to topics such as the role of the data scientist in projects, managing results, and even designing presentations. In addition to working out how to code up models, the book shares how to collaborate with diverse teams, how to translate business goals into metrics, and how to organize work and reports. If you want to learn how to use R to work as a data scientist, get this book.

We have known Nina Zumel and John Mount for a number of years. We have invited them to teach with us at Singularity University. They are two of the best data scientists we know. We regularly recommend their original research on cross-validation and impact coding (also called target encoding). In fact, chapter 8 of Practical Data Science with R teaches the theory of impact coding and uses it through the author’s own R package: vtreat.

Practical Data Science with R takes the time to describe what data science is, and how a data scientist solves problems and explains their work. It includes careful descriptions of classic supervised learning methods, such as linear and logistic regression. We liked the survey style of the book and extensively worked examples using contest-winning methodologies and packages such as random forests and xgboost. The book is full of useful, shared experience and practical advice. We notice they even include our own trick of using random forest variable importance for initial variable screening.

Overall, this is a great book, and we highly recommend it.

—JEREMY HOWARD
AND RACHEL THOMAS