Practical Data Science with R (Paperback)
Nina Zumel, John Mount
立即出貨 (庫存 < 3)
貴賓價: $1,813An Introduction to Statistical Learning: With Applications in R (Hardcover)
貴賓價: $1,568Big Data: Principles and best practices of scalable realtime data systems (Paperback)
貴賓價: $1,749Machine Learning with R (Paperback)
貴賓價: $1,616Simulation for Data Science with R
貴賓價: $1,748Mastering Data Analysis with R (Paperback)
售價: $945R for Everyone: Advanced Analytics and Graphics (Paperback)
貴賓價: $1,710A First Course in Machine Learning, 2/e (Hardcover)
貴賓價: $1,330R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Paperback)
貴賓價: $1,676Make Your Own Neural Network (Paperback)
貴賓價: $1,881R in Action: Data Analysis and Graphics with R, 2/e (Paperback)
貴賓價: $1,767The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2/e (Hardcover)
售價: $1,361Android Apps with App Inventor: The Fast and Easy Way to Build Android Apps (Paperback)
售價: $1,120Google App Inventor
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll 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.
About the Book
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 data science tasks without a lot of academic theory or advanced mathematics.
Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.
This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.
- Data science for the business professional
- Statistical analysis using the R language
- Project lifecycle, from planning to delivery
- Numerous instantly familiar use cases
- Keys to effective data presentations
About the Authors
Nina Zumel and John Mount are cofounders of 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.
Table of Contents
PART 1 INTRODUCTION TO DATA SCIENCE
PART 2 MODELING METHODS
PART 3 DELIVERING RESULTS
- The data science process
- Loading data into R
- Exploring data
- Managing data
- Choosing and evaluating models
- Memorization methods
- Linear and logistic regression
- Unsupervised methods
- Exploring advanced methods
- Documentation and deployment
- Producing effective presentations