000			05060cam a22004218i 4500
001			22144686
003			OSt
005			20221111115751.0
008			210721s2021 flu b 001 0 eng
010			_a 2021019163
020			_a9780367748456 _q(hardback)
020			_a9780367748432 _q(paperback)
020			_z9781003159834 _q(ebook)
040			_aDLC _beng _erda _cJKRC
082	0	0	_a519.50285536 _223 _bAGR
100	1		_aAgresti, Alan, _eAuthor. _918285
245	1	0	_aFoundations of statistics for data scientists : _bwith R and Python / _cby Alan Agresti and Maria Kateri.
250			_a1st
260			_aBoca Raton : _bCRC Press, _c2021.
263			_a2111
300			_axvii, 467 p. ; _c26 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
365			_b74.99 _cPounds
490	0		_aChapman & Hall/CRC texts in statistical science
504			_aIncludes bibliographical references and index.
520			_a"Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python 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 modelling. 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. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises. Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, including Categorical Data Analysis (Wiley) and Statistics: The Art and Science of Learning from Data (Pearson), and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and the Statistician of the Year from the American Statistical Association (Chicago chapter). Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University, authored the monograph Contingency Table Analysis: Methods and Implementation Using R (Birkhäuser/Springer) and a textbook on mathematics for economists (in German). She has a long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, and Business Administration and Engineering. "The main goal of this textbook is to present foundational statistical methods and theory that are relevant in the field of data science. The authors depart from the typical approaches taken by many conventional mathematical statistics textbooks by placing more emphasis on providing the students with intuitive and practical interpretations of those methods with the aid of R programming codes...I find its particular strength to be its intuitive presentation of statistical theory and methods without getting bogged down in mathematical details that are perhaps less useful to the practitioners" (Mintaek Lee, Boise State University) "The aspects of this manuscript that I find appealing: 1. The use of real data. 2. The use of R but with the option to use Python. 3. A good mix of theory and practice. 4. The text is well-written with good exercises. 5. The coverage of topics (e.g. Bayesian methods and clustering) that are not usually part of a course in statistics at the level of this book." (Jason M. Graham, University of Scranton)"-- _cProvided by publisher.
650		0	_aMathematical analysis _xStatistical methods. _918286
650		0	_aQuantitative research _xStatistical methods. _918287
650		0	_aR (Computer program language) _918288
650		0	_aPython (Computer program language) _918289
700	1		_aKateri, Maria, _eAuthor. _918290
776	0	8	_iOnline version: _aAgresti, Alan. _tFoundations of statistics for data scientists _b1st edition. _dBoca Raton : CRC Press, 2021 _z9781003159834 _w(DLC) 2021019164
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _c1 _e23 _n0
999			_c411517 _d411517