Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)

Benjamin S. Baumer is an associate 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 Analyzing Baseball Data with R. He received the 2019 Waller Education Award and the 2016 Significant Contributor Award from the Society for American Baseball Research.

Daniel T. Kaplan is the DeWitt Wallace emeritus professor of mathematics and computer science at Macalester College. He is the author of several textbooks on statistical modeling and statistical computing. Danny received the 2006 Macalester Excellence in Teaching award and the 2017 CAUSE Lifetime Achievement Award.

Nicholas J. Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He is a Fellow of the ASA and the AAAS, co-chair of the National Academies 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 data science curriculum efforts to help students think with data .

Title: Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)

Author: Horton, Nicholas J.,Kaplan, Daniel T.,Baumer, Benjamin S.

ISBN: 9780367191498

Binding:

Publisher: Taylor & Francis Ltd

Publication Date: 2021-04-14

Number of Pages: 632

Weight: 1.5004 kg

This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data. (Hunter Glanz Cal Poly San Luis Obispo)

Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills. (Lynn Collen, St. Cloud Stat Univ.)

I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much...I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R...and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course. (Matthew Beckman, Penn State University)

The authors have successfully completed the job of choosing the content with relevant topics and, deciding the extent of knowledge to be delivered, and finally, putting them in an understandable sequence. This is a well-written book and does not cover much theory. .. The book's second edition contents are updated, expanded, revised, split, rewritten and rearranged compared to the first edition. The key changes are the use of recently developed R packages, .... (and) updated exercises in the chapters ...
-Shalabh, in Journal of the Royal Statistical Society Series A, August 2021

[This book] provides an excellent basis for statisticians who want to dig deeper into, for example, data handling, for computer scientists who aim to strengthen their knowledge of statistical methods as well as for all other researchers who are interested in data science in general. ... Each section is structured as an interplay between R-code and explanatory text for understanding. The division into several stand-alone segments is an advantage, because the reader may easily choose the section she or he is interested in without missing relevant information. A key feature of the book is its focus on different example data sets that are available via R-packages or from URLs that are embedded in the text. These data sets are used to illustrate the methodology presented using R-code. Their availability allows the reader to reproduce the code while working with the book. ... It can be warmly recommended to practical researchers who seek a comprehensive overview of different topics in data science with focus on implementations in R.
-Annika Hoyer, in Biometrical Journal, August 2021

This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data.
-Hunter Glanz, Cal Poly San Luis Obispo

Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills.
-Lynn Collen, St. Cloud Stat University

I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much...I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R...and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course.
-Matthew Beckman, Penn State University