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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 analysis
Audience
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
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 analysis
Audience
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