Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science.
The Big R-Book: From Data Science to Learning Machines and Big Data includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling and exploring data. In Part 5 we learn to build models, Part 6 introduces the reader to the reality in companies, Part 7 covers reports and interactive applications and Part 8 introduces the reader to big data and performance computing. The appendices focus on specialist topics such as building your own extention for R, answer questions that appear througout the book, etc.
- Provides a practical guide for non-experts with a focus on business users
- Contains a unique combination of topics including an introduction to R, machine learning, multi criteria decision analysis, mathematical models, data wrangling, and reporting
- Uses a practical tone and integrates multiple topics in a coherent framework
- Demystifies the hype around machine learning and AI by enabling readers to understand the models and program them in R
- Shows readers how to visualize results in reports and dynamic websites
- Supplementary materials include PDF slides based on the book's content on an Wiley Instructor-only Book Companion Site, as well as all the extracted R-code available to everyone on a Wiley Student Book Companion Site
The Big R-Book is an excellent guide for science technology, engineering, or mathematics students and graduates who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models or review them.