"Modern Data Science (R Language ● 2nd Edition)" is a comprehensive data science textbook for undergraduates that uses a combination of statistical and computational methods to solve real-world data problems. This book not only focuses on examples or programming syntax, but also shows how to use statistical programming in the latest R/RStudio computing environment to extract meaningful information from various data to solve major problems.
This book is a comprehensive update of the previous edition, keeping pace with the increasingly powerful tidyverse package, incorporating new features in packages such as sf, purrr, tidymodels, and tidytext. The content and formatting of the code have been modified for ease of reading and understanding. Some chapters have been split, reorganized, and reimagined to adapt to changing practice environments.
This book is written for readers who want to learn the proper skills to tackle complex data science projects and "think with data" (coined by Diane Lambert). The desire to use data to solve problems is at the heart of the approach presented in this book.
We acknowledge that it is impossible to cover all of these topics at this level of detail in a single book: many chapters could effectively be the basis for a course or series of courses. Instead, our goal is to lay the groundwork for the analysis of real-world data and to see statistics and data analysis in action. After reading this book, readers will greatly expand their ability to work with this data and develop new confidence in their ability to continually learn new techniques.
This book was originally intended to support a one-semester (13-week) upper-division course in Data Science. We also think that this book will be very useful for more advanced students of the discipline as well as for analysts who want to improve their data science skills. In the meantime, casual readers with no programming or statistics experience can read Part I of the book.
