Modern Data Science with R

From reading this book, it is easy to tell that the authors made an effort to add as much value to the reader as possible by going above what they had to do. An obvious example is the numerous warning messages throughout the text that try to steer the reader away from common mistakes. Another example that I really appreciate is the chapter on data science ethics that covers ideas like not making deceptive figures (intentionally or unintentionally) or making sure that predictive models are unbiased. For the actual R content, most of the programming uses the Tidyverse, although the authors are not afraid to create a function. The book covers the major topics you would expect: data manipulation, data visualization, statistics, predictive modeling, and machine learning in general. In addition to these sections, we are given valuable chapters on iteration methods, simulation, databases, among other niche topics. This is to say that the book is a fairly comprehensive review of topics data science practitioners would be interested in. The book can be read for free at this link, https://mdsr-book.github.io/mdsr2e/, or if you prefer a physical copy you can purchase it through this commissioned link: https://amzn.to/3OlnNdk.