Practical Data Science with R

Practical Data Science with R

Nina Zumel, John Mount


Summary

Practical facts 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 situations you will face as you gather, curate, and research the information the most important to the luck of your online business. you are going to practice the R programming language and statistical research recommendations to scrupulously defined examples established in advertising, company intelligence, and determination support.

Purchase of the print e-book incorporates a loose publication in PDF, Kindle, and ePub codecs from Manning Publications.

About the Book

Business analysts and builders are more and more accumulating, curating, studying, and reporting on an important enterprise info. The R language and its linked instruments offer a simple technique to take on daily information technology projects with no lot of educational conception or complex mathematics.

Practical info technology with R exhibits you ways to use the R programming language and worthy statistical ideas to daily enterprise occasions. utilizing examples from advertising and marketing, company intelligence, and selection aid, it exhibits you the way to layout experiments (such as A/B tests), construct predictive versions, and current effects to audiences of all levels.

This publication is obtainable to readers and not using a historical past in info technological know-how. a few familiarity with simple information, R, or one other scripting language is assumed.

What's Inside

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

About the Authors

Nina Zumel and John Mount are cofounders of a San Francisco-based facts technology consulting company. either carry PhDs from Carnegie Mellon and weblog on records, likelihood, and laptop technological know-how at win-vector.com.

Table of Contents

    PART 1 creation TO facts SCIENCE
  1. The information technological know-how process
  2. Loading information into R
  3. Exploring data
  4. Managing data
  5. PART 2 MODELING METHODS
  6. Choosing and comparing models
  7. Memorization methods
  8. Linear and logistic regression
  9. Unsupervised methods
  10. Exploring complicated methods
  11. PART three offering RESULTS
  12. Documentation and deployment
  13. Producing potent presentations

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