Practical Data Science With R #

John Mount and I are proud to present our book, Practical Data Science with R, 2nd Edition. This is the book for you if you are a data scientist, want to be a data scientist, work with data scientists, or hire data scientists.

Our goal is to present data science from a pragmatic, practice-oriented viewpoint. The book will complement other analytics, statistics, machine learning, data science and R books with the following features:

• This book teaches you how to work as a data scientist. Learn how important listening, collaboration, honest presentation and iteration are to what we do.

• The key emphasis of the book is process: collecting requirements, loading data, examining data, building models, validating models, documenting and deploying models to production.

• We provide over 10 significant example datasets, and demonstrate the concepts that we discuss with fully worked exercises using standard R methods. We feel that this approach allows us to illustrate what we really want to teach and to demonstrate all the preparatory steps necessary to any real-world project.

• This book is scrupulously correct on statistics, but presents topics in the context and order a practitioner worries about them. We emphasize machine learning and prediction over the use of summary statistics.

In support of Practical Data Science with R, 2nd Edition, we are providing:

• The Table of Contents, and a free example chapter available from the Manning book page.

• A public repository of data sets and code, under a Creative Commons Attribution-NonCommercial 3.0 Unported License where possible.

• Downloadable example code.

For more about the book please see the following posts from the Win-Vector blog:

• On Writing Our Book: A Little Philosophy -- A note on "prerequisites"

• How Does Practical Data Science with R Stand Out? -- Some other great, useful books for data scientists, and why our book complements them.

Order Practical Data Science With R, 2nd Edition now on the Manning book page.