Publisher's Synopsis
Summary
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. Foreword by Jeremy Howard and Rachel Thomas
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively.
About the Book
Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you'll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you'll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations.
What's inside
- Statistical analysis for business pros
- Effective data presentation
- The most useful R tools
- Interpreting complicated predictive models
About the Reader
You'll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language.
About the Author
Nina Zumel and John Mount founded a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science.
Table of Contents
- The data science process
- Starting with R and data
- Exploring data
- Managing data
- Data engineering and data shaping
- Choosing and evaluating models
- Linear and logistic regression
- Advanced data preparation
- Unsupervised methods
- Exploring advanced methods
- Documentation and deployment
- Producing effective presentations
