Practical Data Science with R
Practical Data Science with R by Nina Zumel, published by Manning Publications on April 13, 2014, is a comprehensive guide designed for business analysts and developers looking to enhance their data science skills. This first edition spans 416 pages and is presented in English. The book focuses on practical applications of the R programming language and statistical analysis techniques, providing readers with real-world use cases in marketing, business intelligence, and decision support without delving into complex theoretical concepts.
Readers will find a straightforward approach to data science, emphasizing the importance of collecting, curating, and analyzing business data. The content covers essential topics such as designing experiments, building predictive models, and effectively presenting results to diverse audiences. With a focus on accessibility, the book assumes some familiarity with basic statistics or scripting languages, making it suitable for those new to data science. Practical Data Science with R serves as a valuable resource for professionals seeking to navigate the project lifecycle from planning to delivery while utilizing R for statistical analysis and data processing.
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Summary
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.
What’s Inside
- 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
- 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
PART 2 MODELING METHODS
PART 3 DELIVERING RESULTS
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