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