Practical Data Science with R: ACM SIGACT News Book Review and Discount!

March 19, 2017
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(This article was originally published at Statistics – Win-Vector Blog, and syndicated at StatsBlogs.)

Our book Practical Data Science with R has just been reviewed in Association for Computing Machinery Special Interest Group on Algorithms and Computation Theory (ACM SIGACT) News by Dr. Allan M. Miller (U.C. Berkeley)!


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The book is half off at Manning March 21st 2017 using the following code (please share/Tweet):

Deal of the Day March 21: Half off my book Practical Data Science with R. Use code dotd032117au at https://www.manning.com/dotd

Please read on for links and excerpts from the review.

We are really excited that our book for practitioners is getting some academic / professional-association attention.

The primary link for the ACM SIGACT review is here (but paywalled): doi :10.1145/3061640.3061644. However, editor Professor Fred Green eventually shares the reviews here (not up yet, but hopefully soon).

Dr. Nina Zumel and I worked very hard work through lot of substantial material into the book. It was a lot of work and requires some effort to work through. Please treat the book as a series of lectures and worked examples. Many readers have reported back that working through the book really pays off. We are thrilled with the reception the book has been getting.

Here are some excerpts from the review:

Zumel and Mount’s book “Practical Data Science with R” provides exactly what the title says: practical coverage of commonly used data science methods using the R programming language. Topics include: how to conduct data science projects, manage and explore datasets, choose and evaluate modeling methods, and present results. The R programming language is used throughout the book for managing data, building models, and generating both graphical and tabular results. The book includes useful appendices on basic R language and tools, and relevant statistical concepts.

The book can serve as a useful advanced introduction to data science for readers with at least a basic understanding of statistics and computer programming. However, it is not designed to be a beginner’s introduction. Readers seeking a purely introductory text would likely find it difficult to grasp many of the concepts covered, but at least can get a valuable overview of the subject. Most would likely return to the book many times as their level of understanding and experience doing data science grows.

“Practical Data Science with R” is a remarkable book, packed with both valuable technical material about data science, and practical advice for how to conduct a successful data science project. In a field that is so new, and growing so quickly, it is an essential guide for practitioners, especially for the large numbers of new data scientists moving into the field. It is not only a worthwhile read, it can serve as a useful ongoing technical reference and practical manual for the data science practitioner.

A lot of books cover tools and notation, we took a lot of effort to discuss what is needed to get a great data science result, and how to do it with R.



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