*Modern Data Science with R* is a 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.

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Modern Data Science with R” is a landmark: the first full textbook in data science…Indeed, if R were to cease to exist tomorrow, these readers would still be well-situated to be data scientists. In a nutshell, that approach is what makes this such a successful textbook (not a handbook) suited for a course (not a workshop) on data science (not statistics). (Andrew Bray, Reed College)

“The book is unique. It is an encyclopedia of Data Science, and it covers a wide variety of modern topics; another positive aspect is that it contains lots of examples and code, and the layout is quite catchy. One can learn (and teach) subjects as diverse as: How to give talks, administrating databases, how to model spatial data, and even ethics—all in one book.” (Miguel de Carvalho, The University of Edinburgh)

“This book is unique because it incorporates theoretical fundamentals such as statistical learning and regression modelling with the modern, practical elements of data science, including setting up databases and debugging.

Modern Data Science with Rpresents a variety of topics with several illustrative and engaging examples in R. This book is a valuable resource to all those studying and interested in data science.” (Shuangzhe Liu, University of Canberra)