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Nina Zumel and John Mount have been working very hard on producing an exciting new book called “Practical Data Science with R.” The book has now entered Manning Early Access Program (MEAP) which allows you to subscribe to chapters as they become available and give us feedback before the book goes into print.

This is the book for you if you are a data scientist, want to be a data scientist, work with data scientists, or even pay data scientists.

Please subscribe to our book, your support now will help us improve it. Please also forward this offer to your friends and colleagues (and please ask them to also subscribe and forward).

This is going to be a great book. It is going to complement other analytics, statistics, machine learing, data science and R books with the following features:

  • This book will teach you how to work as a data scientist. Learn how important listening, collaboration, honest presentation and iteration are in this work.
  • The key emphasis of the book is process: collecting requirements, loading data, examining data, building models, validating models, documenting and deploying models to production
  • Pretty much all steps discussed in the book will have working R commands to call standard R methods. While this book is not a set of case studies we will supply example data.
  • This book will be scrupulously correct on statistics, but present topics in the context and order a practitioner worries about them. We are going to emphasize machine learning and prediction over the use of summary statistics.

In support of Practical Data Science with R we are supplying:

The final e-version and print versions of this book will be professionally edited and typeset (early-access drafts are directly from the authors and roughly formatted using an automated Docbook workflow). To keep the price down the final print book will be in black and white (we will make sure all graphs are completely legible in black and white) with an accompanying full color e-version.

Buy at http://www.win-vector.com/blog/practical-data-science-with-r/

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