Practical Data Science with R

Practical Data Science with R

Nina Zumel, John Mount


Practical info technology with R lives as much as its identify. It explains simple rules with no the theoretical mumbo-jumbo and jumps correct to the genuine use circumstances you are going to face as you gather, curate, and examine the knowledge the most important to the luck of your corporation. you will practice the R programming language and statistical research options to rigorously defined examples established in advertising, company intelligence, and determination support.

Purchase of the print ebook contains a loose book in PDF, Kindle, and ePub codecs from Manning Publications.

About the Book

Business analysts and builders are more and more accumulating, curating, examining, and reporting on the most important enterprise information. The R language and its linked instruments supply a simple method to take on day by day info technological know-how projects with no lot of educational idea or complicated mathematics.

Practical information technological know-how with R indicates you the way to use the R programming language and important statistical suggestions to daily company events. utilizing examples from advertising and marketing, company intelligence, and choice help, it exhibits you the way to layout experiments (such as A/B tests), construct predictive types, and current effects to audiences of all levels.

This booklet is out there to readers and not using a heritage in facts technological know-how. a few familiarity with simple data, R, or one other scripting language is assumed.

What's Inside

  • Data technological know-how for the enterprise professional
  • Statistical research utilizing the R language
  • Project lifecycle, from making plans to delivery
  • Numerous immediately customary use cases
  • Keys to potent facts presentations

About the Authors

Nina Zumel and John Mount are cofounders of a San Francisco-based information technology consulting enterprise. either carry PhDs from Carnegie Mellon and weblog on records, likelihood, and desktop technology at

Table of Contents

    PART 1 creation TO facts SCIENCE
  1. The info technology process
  2. Loading information into R
  3. Exploring data
  4. Managing data
  6. Choosing and comparing models
  7. Memorization methods
  8. Linear and logistic regression
  9. Unsupervised methods
  10. Exploring complex methods
  11. PART three providing RESULTS
  12. Documentation and deployment
  13. Producing potent presentations

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