Modern Data Science with R, 2nd Edition

Introducing the all-new second edition of “Modern Data Science with R,” designed to enhance your understanding and application of data science principles using the R programming language.

Key Features:

– ISBN-10: 0367191490
– ISBN-13: 978-0367191498

From a review of the first edition: “Modern Data Science with R… is rich with examples and is guided by a robust narrative voice. Moreover, it presents an organizing framework that establishes data science as a distinct discipline from applied statistics” (The American Statistician).

Modern Data Science with R is a comprehensive textbook that combines statistical and computational thinking to address real-world data challenges. Rather than focusing solely on case studies or programming syntax, this book demonstrates how statistical programming in the cutting-edge R/RStudio environment can be utilized to extract valuable insights from diverse datasets to tackle complex questions.

The second edition has been updated to incorporate the growing influence of the tidyverse package. All code in the book has been revised and formatted to enhance readability and comprehension. New features from packages like sf, purrr, tidymodels, and tidytext have been integrated into the content. Each chapter has been reviewed, with some sections reorganized or reimagined to align with current best practices in the field.

Frequently Asked Questions (FAQs):

Q: Is this book suitable for undergraduate students?
A: Yes, “Modern Data Science with R” is tailored for undergraduate students looking to apply statistical and computational concepts to solve data-driven problems.

Q: Are there any specific updates in the second edition?
A: The second edition features new content on the tidyverse package and its applications, along with revamped code examples for improved clarity.

Q: Who are the authors of this book?
A: The book is authored by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton, renowned experts in the field of statistical modeling and data science.

About the Authors:

Benjamin S. Baumer is an associate professor in the Statistical & Data Sciences program at Smith College. With extensive experience as a data scientist, Ben has contributed to notable publications in the field of statistics and data analysis.

Daniel T. Kaplan is the DeWitt Wallace emeritus professor of mathematics and computer science at Macalester College. He has authored several textbooks on statistical modeling and computing, receiving prestigious awards for his contributions to teaching and research.

Nicholas J. Horton is a distinguished professor at Amherst College, recognized for his expertise in statistics and data science. With a focus on enhancing data literacy among students, Nicholas has received numerous accolades for his dedication to advancing statistical education.

In conclusion, “Modern Data Science with R, 2nd Edition” is a must-have resource for students and professionals seeking to expand their knowledge and skills in the field of data science, offering practical insights and hands-on experience with the R programming language.