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, practiceoriented 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 realworld 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 AttributionNonCommercial 3.0 Unported License where possible.

Downloadable example code.
For more about the book please see the following posts from the WinVector 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.