Modern Data Science with R, 2nd Edition

If you are looking to delve into the world of data science using R, the “Modern Data Science with R, 2nd Edition” textbook is a must-have resource. This comprehensive book is designed for undergraduates and integrates statistical and computational thinking to solve real-world data problems. Instead of just focusing on case studies or programming syntax, this book demonstrates how statistical programming in R/RStudio can be used to extract valuable insights from diverse datasets.

The second edition of “Modern Data Science with R” has been updated to incorporate the latest tools and techniques from the tidyverse package. The code examples in the book have been revised for readability and comprehension. New functionality from packages like sf, purrr, tidymodels, and tidytext has been integrated into the text. Chapters have been restructured to align with current best practices in the field of data science.

Regarding the Authors

– Benjamin S. Baumer: An associate professor at Smith College, Benjamin has been a practicing data scientist since 2004. He is the co-author of “The Sabermetric Revolution” and “Analyzing Baseball Data with R.”

– Daniel T. Kaplan: Emeritus professor of mathematics and computer science at Macalester College, Daniel has authored several textbooks on statistical modeling and computing.

– Nicholas J. Horton: Beitzel Professor of Technology and Society at Amherst College, Nicholas is a respected figure in the field of statistics and data science, actively involved in promoting data science education.

Whether you are a student, researcher, or practitioner in the field of data science, “Modern Data Science with R, 2nd Edition” is a valuable resource that will enhance your skills and knowledge in this rapidly evolving field.

FAQs

1. Who is the target audience for “Modern Data Science with R, 2nd Edition”?

This textbook is designed for undergraduate students interested in data science, as well as professionals and researchers looking to enhance their skills in statistical programming using R.

2. What sets the second edition of the book apart from the first edition?

The second edition has been updated to include new tools and techniques from the tidyverse package, making the code examples more readable and relevant to current industry practices.

3. Are there any notable authors associated with the book?

Yes, the book is authored by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton, who are all experts in the fields of statistics, data science, and education.

Conclusion

“Modern Data Science with R, 2nd Edition” is a comprehensive textbook that offers a unique blend of statistical and computational thinking to tackle real-world data challenges. With updated content, revised code examples, and contributions from top experts in the field, this book is an essential resource for anyone looking to excel in the field of data science using R. Whether you are a student, researcher, or practitioner, this book will equip you with the knowledge and skills needed to succeed in the dynamic world of data science.