Practical Data Science with R

This invaluable addition to any data scientists library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. 

Practical Data Science with R, Second Edition takes a practice oriented approach to explaining basic principles in the ever-expanding field of data science. Youll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. 

Key features

Data science and statistical analysis for the business professional 

Numerous instantly familiar real-world use cases 

Keys to effective data presentations 

Modeling and analysis techniques like boosting, regularized regression, and quadratic                                 discriminant analysis

Audience

While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science.

About the technology

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 

Nina Zumel and John Mount are co-founders of Win-Vector LLC, 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.