March 2014
Beginner to intermediate
416 pages
13h 36m
English
Overview
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.
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 data science tasks without a lot of academic theory or advanced mathematics.
About the Book
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.
What's Inside
- Data science for the business professional
- Statistical analysis using the R language
- Project lifecycle, from planning to delivery
- Numerous instantly familiar use cases
- Keys to effective data presentations
About the Reader
This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.
About the Authors
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.
Quotes
A unique and important addition to any data scientist’s library.
- From the Foreword by Jim Porzak, Cofounder Bay Area R Users Group
Covers the process end-to-end, from data exploration to modeling to delivering the results.
- Nezih Yigitbasi, Intel
Full of useful gems for both aspiring and experienced data scientists.
- Fred Rahmanian, Siemens Healthcare
Hands-on data analysis with real-world examples. Highly recommended.
- Dr. Kostas Passadis, IPTO
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