000			02173cam a2200301 i 4500
003			RNL
005			20260330044826.0
008			201130s2021 flu ob 001 0 eng
020			_a9781032941677
040			_aRCL
082	0	0	_a006.312 B341M
100	1		_aBaumer, Benjamin, _930928
245	1	0	_aModern Data Science With R _c/ Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton.
250			_a2nd.
260			_aOxford: _bCRC Press, _c2025.
300			_axvii, 631p. ; 24cm. _billustration;
490	0		_aChapman and Hall/CRC Press texts in statistical science
504			_aIncludes bibliographical references and indexes.
520			_a"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)"--
546			_aEnglish
650		0	_aData mining. _930929
650		0	_aBig data. _928489
650		0	_aMathematical statistics _930768
650		0	_aR (Computer program language) _930764
700	1		_aKaplan, Daniel, _930930
700	1		_aHorton, Nicholas J., _930931
942			_cBK
999			_c48029 _d48029