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Practical Data Science with R
Nina Zumel and John Mount
Foreword by Jim Porzak
March 2014 | 416 pages
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.
Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.
This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.
Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.
appendix A Working with R and other tools
appendix B Important statistical concepts
appendix C More tools and ideas worth exploring
Note to readers about the paper edition of this book. The plots and graphs are in grey scale. I wrote to the authors about this and they were kind enough to respond:
I paid for and have both the PDF and paper editions. Overall, I think the authors do a good job with the book. The content is helpful, interesting, and decently written. The PDF edition is beautiful and engaging with charts and graphs that are easy to read, but it's electronic format.
The big miss with the book is the presentation of the plots and graphs in the the paper edition. Because there is no color, the experience is taxing and dull. The paper edition hinders cognition rather than enabling it. I haven't picked up the paper edition since my first experience with it.