Modern Data Science with R, 2nd Edition
Original price was: $60.00.$53.97Current price is: $53.97.
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Resource Type: Test bank
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ISBN-100367191490
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ISBN-13978-0367191498
From a overview of the first model: “Progressive Data Science with R… is rich with examples and is guided by a sturdy narrative voice. Moreover, it presents an organizing framework that makes a convincing argument that info science is a course distinct from utilized statistics” (The American Statistician).
Progressive Data Science with R is an entire info science textbook for undergraduates that incorporates statistical and computational contemplating to resolve real-world info factors. Reasonably than focus fully on case analysis or programming syntax, this e-book illustrates how statistical programming within the cutting-edge R/RStudio computing environment may be leveraged to extract vital information from a range of data within the service of addressing compelling questions.
The second model is updated to reflect the rising affect of the tidyverse set of packages. All code within the e-book has been revised and styled to be more readable and easier to comprehend. 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.
Regarding the Author
Benjamin S. Baumer is an associate professor in the Statistical & Data Sciences program at Smith College. He has been a practicing info 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 Teaching Award and the 2016 Essential Contributor Award from the Society for American Baseball Evaluation.
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 numerous national teaching awards, author of a series of books on statistical computing, and actively involved in info science curriculum efforts to support students “think with info”.
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