Modern Data Science with R, 2/e

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

  • 出版商: CRC
  • 出版日期: 2021-04-14
  • 售價: $3,800
  • 貴賓價: 9.5$3,610
  • 語言: 英文
  • 頁數: 632
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 0367191490
  • ISBN-13: 9780367191498
  • 相關分類: Data Science
  • 相關翻譯: 現代數據科學(R語言·第2版) (簡中版)
  • 立即出貨 (庫存=1)



From a review of the first edition: "Modern Data Science with R... 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" (The American Statistician).

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. 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 questions.


The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.





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".





Benjamin S. Baumer是史密斯學院統計與數據科學計劃的副教授。自2004年成為紐約大都會隊的首位全職統計分析師以來,他一直是一名實踐數據科學家。Ben是《The Sabermetric Revolution》和《Analyzing Baseball Data with R》的合著者。他獲得了2019年Waller教育獎和2016年美國棒球研究學會的重要貢獻獎。

Daniel T. Kaplan是麥卡萊斯特學院數學和計算機科學的DeWitt Wallace名譽教授。他是幾本關於統計建模和統計計算的教科書的作者。Danny獲得了2006年麥卡萊斯特卓越教學獎和2017年CAUSE終身成就獎。

Nicholas J. Horton是阿默斯特學院貝茲爾技術與社會(統計與數據科學)教授。他是美國統計協會和美國科學促進會的會士,是國家科學院應用和理論統計委員會的聯席主席,獲得了多個國家教學獎項,是一系列關於統計計算的書籍的作者,並積極參與數據科學課程的努力,幫助學生“以數據思考”。