| Practical Data Science with R Contributor(s): Zumel, Nina (Author), Mount, John (Author) |
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ISBN: 1617295876 ISBN-13: 9781617295874 Publisher: Manning Publications OUR PRICE: $47.49 Product Type: Paperback Published: December 2019 |
| Additional Information |
| BISAC Categories: - Computers | Databases - Data Mining - Computers | Software Development & Engineering - Systems Analysis & Design - Computers | Software Development & Engineering - Quality Assurance & Testing |
| Dewey: 005.133 |
| Physical Information: 1.1" H x 7.4" W x 9.1" (2.00 lbs) 483 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. |
Contributor Bio(s): Mount, John: - 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. Zumel, Nina: -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. |