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John Mount
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Nina Zumel
– author
Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the Technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the Book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's Inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the Reader You’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the Authors Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science. Quotes Full of useful shared experience and practical advice. Highly recommended. - From the Foreword by Jeremy Howard and Rachel Thomas Great examples and an informative walk-through of the data science process. - David Meza, NASA Offers interesting perspectives that cover many aspects of practical data science; a good reference. - Pascal Barbedor, BL SET R you ready to get data science done the right way? - Taylor Dolezal, Disney Studios |
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Practical Data Science with R
2014-03-25
John Mount
– author
,
Nina Zumel
– author
NEWER EDITION AVAILABLE IN MEAP Practical Data Science with R, Second Edition is now available in the Manning Early Access Program. An eBook of this older edition is included at no additional cost when you buy the revised edition! You may still purchase Practical Data Science with R (First Edition) using the Buy options on this page. 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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