Sale!

Modern Data Science with R 2nd Edition – PDF ebook

Modern Data Science with R 2nd Edition – PDF ebook Copyright: 2021, Edition: 2nd, Author: Benjamin S. Baumer; Daniel T. Kaplan; Nicholas J. Horton, Publisher: Chapman & Hall, Print ISBN: 9780429200717, etext ISBN: 9780429575396, Format: PDF

Original price was: $99.00.Current price is: $23.00.

SKU: 9780429200717 Category: Tag:

Buy Modern Data Science with R 2nd Edition PDF ebook by author Benjamin S. Baumer; Daniel T. Kaplan; Nicholas J. Horton – published by Chapman & Hall in 2021 and save up to 80%  compared to the print version of this textbook. With PDF version of this textbook, not only save you money, you can also highlight, add text, underline add post-it notes, bookmarks to pages, instantly search for the major terms or chapter titles, etc.
You can search our site for other versions of the Modern Data Science with R 2nd Edition PDF ebook. You can also search for others PDF ebooks from publisher Chapman & Hall, as well as from your favorite authors. We have thousands of online textbooks and course materials (mostly in PDF) that you can download immediately after purchase.
Note: e-textBooks do not come with access codes, CDs/DVDs, workbooks, and other supplemental items.
eBook Details:

Full title: Modern Data Science with R 2nd Edition
Edition: 2nd
Copyright year: 2021
Publisher: Chapman & Hall
Author: Benjamin S. Baumer; Daniel T. Kaplan; Nicholas J. Horton
ISBN: 9780429200717, 9780429575396
Format: PDF

Description of Modern Data Science with R 2nd Edition:
From a review of the first “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.