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.

About the Author

Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and won the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research.

Daniel T. Kaplan is the DeWitt Wallace professor of mathematics and computer science at Macalester College. He is the author of several textbooks on statistical modeling and statistical computing, and received the 2006 Macalester Excellence in Teaching award.

Nicholas J. Horton is a professor of statistics at Amherst College. He is a Fellow of the American Statistical Association (ASA), member of the NRC Committee on Applied and Theoretical Statistics, recipient of a number of national teaching awards, author of a series of books on statistical computing, and actively involved in curricular reform to help students "think with data."

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