000			02026nam a22002177a 4500
008			240924b |||||||| |||| 00| 0 eng d
020			_a9780367748456 (hb.)
082			_a519.50285536 _bAGR
100			_aAgresti, Alan _912163
245			_aFoundations of statistics for data scientists: _bwith R and Python
260			_aBoca Raton _bCRC Press _c2022
300			_axvii, 467p.
500			_ahttps://www.routledge.com/Foundations-of-Statistics-for-Data-Scientists-With-R-and-Python/Agresti-Kateri/p/book/9780367748456?srsltid=AfmBOoqd7kNzZMKD4vRkjMaiF
509			_a14
520			_a"Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on ""why it works"" as well as ""how to do it."" Compared to traditional ""mathematical statistics"" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python). Contains nearly 500 exercises."
650			_aProgramming Languages _96495
650			_aStatistical Methods
650			_aQuantitative Research _91799
700			_aKateri, Maria _912164
942			_cBK
999			_c10800 _d10800