Practical Data Science with R

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$26.00
UPC:
9781617291562
Maximum Purchase:
3 units
Publisher:
Manning; 1st edition (April 13, 2014)
Binding:
Paperback
Pages:
389
Shipping Weight:
1.5 pounds
Language:
English
Product Dimensions:
7.5 x 1 x 9.5 inches
ISBN-10:
1617291560
ISBN-13:
978-1617291562
Author:
Jim Porzak,Nina Zumel,John Mount

Product Overview

SummaryPractical Data Science with Rlives 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.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the BookBusiness 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 Rshows 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.What's InsideData science for the business professionalStatistical analysis using the R languageProject lifecycle, from planning to deliveryNumerous instantly familiar use casesKeys to effective data presentationsAbout the AuthorsNina ZumelandJohn Mountare 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.Table of ContentsPART 1 INTRODUCTION TO DATA SCIENCEThe data science processLoading data into RExploring dataManaging dataPART 2 MODELING METHODSChoosing and evaluating modelsMemorization methodsLinear and logistic regressionUnsupervised methodsExploring advanced methodsPART 3 DELIVERING RESULTSDocumentation and deploymentProducing effective presentations

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