Practical Data Science With R
John Mount and I are proud to present our book, Practical Data Science with R. 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 we are providing:
- Two free preview chapters and the Table of Contents, all available at the Manning book page.
- A public repository of data sets and code (under a Creative Commons Attribution-NonCommercial 3.0 Unported License where possible).
- An official book forum and errata.
The physical book is 416 pages with black and white figures. The eBook version has color figures and is DRM-free. When you buy the physical version, you will get free access to the eBook version in all three formats: PDF, ePub, and Kindle.
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 “pre-requisites”
- How Does Practical Data Science with R Stand Out? — Some other great, useful books for data scientists, and why our book complements them.