Practical Data Science with R
(Sprache: Englisch)
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...
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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. Key features
- Data science and statistical analysis for the business professional
- Numerous instantly familiar real-world use cases
- Keys to effective data presentations
- Modeling and analysis techniques like boosting, regularized regression, and quadratic discriminant analysisAudience
While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science.About the technology
Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day
Autoren-Porträt von Nina Zumel, John Mount
Nina ZumelNina 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
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.
Bibliographische Angaben
- Autoren: Nina Zumel , John Mount
- 2019, 2. Aufl., 483 Seiten, Maße: 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Manning Publications
- ISBN-10: 1617295876
- ISBN-13: 9781617295874
Sprache:
Englisch
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