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

**Author:** Baumer, Benjamin S.

**Brand:** Chapman and Hall/CRC

**Edition:** 1

**Binding:** Hardcover

**Number Of Pages:** 582

**Release Date:** 02-02-2017

**Details:** Product Description
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.
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
"Only about 60 of the book’s 551 pages address the questions of uncertainty and inference that constitute the core of the statistics tradition. The remaining pages attend the other components of working with data―the import, wrangling, tidying, visualization, and storage―that are often the more prominent barriers to understanding modern datasets...Modern Data Science with R is a landmark: the first full textbook in data science. (It can serve) as the backbone of a semester-long course targeted at students with little background in statistics or computing. It is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics…By using the tidyverse, the textbook authors are able to seamlessly interweave a conceptual framework for data science with the corresponding implementation in R code….Even though this book is heavily dependent on R, readers come away with a more general natural language with which to talk and think about data. 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." ~The American Statistician
"Baumer, Kaplan, and Horton have managed to write a book that will serve a huge variety of educators while being endlessly interesting and useful to students of a modern era. Modern Data Science in R is a compilation of ideas from both ends of the data science and statistics spectrum―tools for setting up databases and working with regular expressions are intermixed with fundamentals like regression analysis. Additionally, the authors pull together fantastic examples from the scientific community as well as the media at large. Their examples will engage today's students into understanding why data wrangling, reproducibility, and ethics are a fundamental part of any data analysis.
Good visualization skills (Tukey) and ethical analyses (Hoff, "How to Lie

**Package Dimensions:** 10.1 x 7.3 x 1.4 inches

**Languages:** English