Practical Data Science with R

Practical Data Science with R

Paperback

By (author) Nina Zumel, By (author) John Mount

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  • Publisher: Manning Publications
  • Format: Paperback | 416 pages
  • Dimensions: 185mm x 231mm x 28mm | 703g
  • Publication date: 13 April 2014
  • Publication City/Country: New York
  • ISBN 10: 1617291560
  • ISBN 13: 9781617291562
  • Sales rank: 139,184

Product description

DESCRIPTION Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions. 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 faced while collecting, curating, and analyzing the data crucial to the success of businesses. Readers will apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels. RETAIL SELLING POINTS Demonstrations of need-to-know statistical ideas Covers all aspects of the project lifecycle Data science for the motivated business professional AUDIENCE Written for the business analyst, technical consultant or technical director- no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus. ABOUT THE TECHNOLOGY R is a programming language which is used for developing statistical software programs. Data Science is the process of collecting data and developing analysis techniques and software over that data to answer key business questions.

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Author information

AUTHOR BIO Nina Zumel and John Mount are co-founders of Win-Vector, a data science consulting firm in San Francisco. Nina 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. John 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. Both contribute to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.