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.
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
Nina Zumel and John Mount are co-founders of Win-Vector LLC, a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at
win-vector.com.
Author: Nina Zumel, John Mount
Publisher: Manning Publications
Published: 12/13/2019
Pages: 483
Binding Type: Paperback
Weight: 2.00lbs
Size: 9.10h x 7.40w x 1.10d
ISBN13: 9781617295874
ISBN10: 1617295876
BISAC Categories:
- Computers | Data Science | Data Analytics
- Computers | Software Development & Engineering | Systems Analysis & Desi
- Computers | Software Development & Engineering | Quality Assurance & Tes
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.