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Practical Data Science with R
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Practical Data Science with R Paperback - 2014

by Nina Zumel; John Mount


From the publisher

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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. 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. 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 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. Table of Contents
    PART 1 INTRODUCTION TO DATA SCIENCE
  1. The data science process
  2. Loading data into R
  3. Exploring data
  4. Managing data
  5. PART 2 MODELING METHODS
  6. Choosing and evaluating models
  7. Memorization methods
  8. Linear and logistic regression
  9. Unsupervised methods
  10. Exploring advanced methods
  11. PART 3 DELIVERING RESULTS
  12. Documentation and deployment
  13. Producing effective presentations

Details

  • Title Practical Data Science with R
  • Author Nina Zumel; John Mount
  • Binding Paperback
  • Edition International Ed
  • Pages 416
  • Volumes 1
  • Language ENG
  • Publisher Manning Publications, Shelter Insland, NY
  • Date 2014-04
  • ISBN 9781617291562 / 1617291560
  • Weight 1.6 lbs (0.73 kg)
  • Dimensions 9.1 x 7.3 x 1 in (23.11 x 18.54 x 2.54 cm)

About the author

Nina Zumel co-founded Win-Vector, a data science consulting firm in San Francisco. She holds a PH.D. in robotics from Carnegie Mellon and was a content developer for EMC's Data Science and Big Data Analytics Training Course. Nina also contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization. John Mount co-founded Win-Vector, a data science consulting firm in San Francisco. He has a Ph.D. in computer science from Carnegie Mellon and over 15 years of applied experience in biotech research, online advertising, price optimization and finance. He contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.
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