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Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)By Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
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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.
- Amazon Sales Rank: #144315 in Books
- Published on: 2017-02-02
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 7.25" w x 1.50" l, .0 pounds
- Binding: Paperback
- 582 pages
Review
"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 with Statistics") are not new ideas. However, they have recently been lost in the drive for more sophisticated mathematical and computational methods for working with data. Baumer et al. modernize the need for good visualization and communication in ways that will resonate with today's practitioners. Like Wickham's "ggplot2" and "The Elements of Statistical Learning" by Hastie et al., "Modern Data Science in R" promises to be a staple on every data analyst's bookshelf. Accessible to students and a valuable resource for those who have been in the field for many years, this book promises to be a treasure you will want to discover." ~ Jo Hardin, Pomona College
"This book would be an excellent text book for an introductory data science course. Many academic institutions are now trying to open data science programs. But, there is not a good text book available for data science courses." ~ Mahbubul Majumder, U. of Nebraska Omaha
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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