Modern Data Science with R
comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions.
Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.
Review
“Modern Data Science with R is one of the first textbooks to provide a comprehensive introduction to data science for students at the undergraduate level (it is also suitable for graduate students and professionals in other fields). The authors follow the approach taken by Garrett Grolemund and Hadley Wickham in their book, R for Data Science, and David Robinson in Teach the Tidyverse to Beginners, which emphasizes the teaching of data visualization and the tidyverse (using dplyr and chained pipes) before covering base R, along with using real-world data and modern data science methods. The textbook includes end of chapter exercises (an instructor’s solution manual is available), and a series of lab activities is also under development. The result is an excellent textbook that provides a solid foundation in data science for students and professionals alike… Modern Data Science with R is a breakthrough textbook.” ~ ACM SIGACT News
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