Nina Zumel and John Mount present their new book, Practical Data Science with R.
About Practical Data Science with R
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.
Nina Zumel started her advanced education in electrical engineering at U.C Berkeley and holds a Ph.D. in robotics from Carnegie Mellon. She has worked as a scientist at SRI International, an independent, nonprofit research institute. She has worked as chief scientist of a price optimization company and founded a contract research company. Nina is now a principal consultant at Win-Vector LLC a San Francisco data science consultancy. Nina is the first author of “Practical Data Science with R” (Manning Publications, 2014), blogs about technical issues at http://www.win-vector.com/blog/ , and writes about myths at http://multoghost.wordpress.com . She can be reached at [masked].
John Mount started his advanced education in mathematics as U.C. Berkeley and holds a Ph.D. in computer science from Carnegie Mellon. He has worked as a computational scientist in biotechnology, as a stock trading algorithm designer, and has managed a research team for Shopping.com (now an eBay). He is now a principal consultant at Win-Vector LLC a San Francisco data science consultancy. John is an author of “Practical Data Science with R” (Manning Publications, 2014), blogs about technical issues at http://www.win-vector.com/blog/ , and is active in the San Francisco Rotary. He can be reached at [masked].
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