Practical Data Science with R

Practical Data Science with R

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<div><p><b>Summary</b></p><p><i>Practical Data Science with R</i> 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.</p><p>Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.</p><p><b>About the Book</b></p><p>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.</p><p><i>Practical Data Science with R</i> 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.</p><p>This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.</p><p><b>What's Inside</b></p><ul><li>Data science for the business professional</li><li>Statistical analysis using the R language</li><li>Project lifecycle, from planning to delivery</li><li>Numerous instantly familiar use cases</li><li>Keys to effective data presentations</li></ul><p><b>About the Authors</b></p><p><b>Nina Zumel</b> and <b>John Mount</b> 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.</p><p><b>Table of Contents</b></p><ol><h5>PART 1 INTRODUCTION TO DATA SCIENCE</h5><li>The data science process</li><li>Loading data into R</li><li>Exploring data</li><li>Managing data</li><h5>PART 2 MODELING METHODS</h5><li>Choosing and evaluating models</li><li>Memorization methods</li><li>Linear and logistic regression</li><li>Unsupervised methods</li><li>Exploring advanced methods</li><h5>PART 3 DELIVERING RESULTS</h5><li>Documentation and deployment</li><li>Producing effective presentations</li></ol></div>