Practical Data Science with R, Second Edition

  • 9h 8m
  • John Mount, Nina Zumel
  • Manning Publications
  • 2020

This invaluable addition to any data scientist's library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more.

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 Authors

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.

In this Book

  • The Data Science Process
  • Starting with R and Data
  • Exploring Data
  • Managing Data
  • Data Engineering and Data Shaping
  • Choosing and Evaluating Models
  • Linear and Logistic Regression
  • Advanced Data Preparation
  • Unsupervised Methods
  • Exploring Advanced Methods
  • Documentation and Deployment
  • Producing Effective Presentations
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