Practical Data Science with R
Seiten
2019
|
2nd edition
Manning Publications (Verlag)
978-1-61729-587-4 (ISBN)
Manning Publications (Verlag)
978-1-61729-587-4 (ISBN)
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.
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.
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.
| Erscheinungsdatum | 30.09.2019 |
|---|---|
| Zusatzinfo | Illustrations, unspecified |
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 240 x 380 mm |
| Gewicht | 613 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| ISBN-10 | 1-61729-587-6 / 1617295876 |
| ISBN-13 | 978-1-61729-587-4 / 9781617295874 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Datenanalyse für Künstliche Intelligenz
Buch | Softcover (2024)
De Gruyter Oldenbourg (Verlag)
CHF 104,90
Auswertung von Daten mit pandas, NumPy und IPython
Buch | Softcover (2023)
O'Reilly (Verlag)
CHF 62,85
