Summary
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 Book
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.
Whatβs 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 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.