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About Practical Data Science With R
Product 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. Review "Full of useful shared experience and practical advice. Highly recommended." --- From the Foreword by Jeremy Howard and Rachel Thomas "Great examples and an informative walk-through of the data science process." --- David Meza, NASA "Offers interesting perspectives that cover many aspects of practical data science; a good reference." --- Pascal Barbedor, BL SET "R you ready to get data science done the right way?" --- Taylor Dolezal, Disney Studios Praise for the 1st edition. ACM SIGACT, Reviewed by Allan M. Miller. doi :10.1145/3061640.3061644 ( dl.acm.org/citation.cfm?doid=3061640.3061644 ) "Practical Data Science with R" is a remarkable book, packed with both valuable technical material about data science, and practical advice for how to conduct a successful data science project. In a field that is so new, and growing so quickly, it is an essential guide for practitioners, especially for the large numbers of new data scientists moving into the field. It is not only a worthile read, it can serve as a useful ongoing technical reference and practical manual for the data science practitioner. From the Author Physical copy is in black and white, e-copy includes color figures. Each physical copy comes with rights to a free download of a complete e-copy. Some of the features new to the 2nd edition include: A chapter on advanced data preparation using the vtreat package. Regularization methods. Model explainability. More on data manipulation and data wrangling. Using xgboost / gradient boosting. About the Author 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.
