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Practical Data Science with R, Second Edition

Nina Zumel and John Mount
Foreword by Jeremy Howard and Rachel Thomas
  • November 2019
  • ISBN 9781617295874
  • 568 pages
  • printed in black & white
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Full of useful shared experience and practical advice. Highly recommended.

From the Foreword by Jeremy Howard and Rachel Thomas
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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 author

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.

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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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