Modern Data Science with R - Info and Reading Options
By Benjamin S. Baumer, Daniel T. Kaplan and Nicholas J. Horton
"Modern Data Science with R" was published by Taylor & Francis Group in 2021 - Boca Raton London New York, it has 632 pages and the language of the book is English.
“Modern Data Science with R” Metadata:
- Title: Modern Data Science with R
- Authors: Benjamin S. BaumerDaniel T. KaplanNicholas J. Horton
- Language: English
- Number of Pages: 632
- Publisher: Taylor & Francis Group
- Publish Date: 2021
- Publish Location: Boca Raton London New York
“Modern Data Science with R” Subjects and Themes:
- Subjects: ➤ Statistics - Data mining - Big data - Mathematical statistics - Data processing - R (Computer program language)
Edition Identifiers:
- The Open Library ID: OL33835829M - OL21601079W
- ISBN-13: 9780429200717 - 9780367191498 - 9780367745448
- All ISBNs: 9780429200717 - 9780367191498 - 9780367745448
AI-generated Review of “Modern Data Science with R”:
"Modern Data Science with R" Description:
Open Data:
"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. 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)"--
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