000			03018cam a2200481 i 4500
001			1096213676
003			OCoLC
005			20250829162702.0
008			190307t20202020flua b 001 0 eng
010			_a2019008196
015			_aGBB9B5674 _2bnb
020			_a9781138393295
020			_a9781138393295
020			_a9781138393295
020			_a036726093X
020			_z9781138393295
020			_z9780429687129
020			_z9780429687105
020			_z9780429401862
024	8		_a16051385
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dNDD _dYDX _dUKMGB _dOCLCQ _dBNG _dS9M _dMUU _dSOI _dYUS _dZAQ _dAJB _dTOH _dTOL _dUtOrBLW
042			_apcc
050	0	0	_aQA273 _b.M38495 2020
082			_a 519.5 MAT 2020
100	1		_aMatloff, Norman S., _eauthor
245	1	0	_aProbability and statistics for data science : _bmath + R + data / _cNorman Matloff
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c[2020]
264		4	_c©2020
300			_axxxii, 412 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aChapman & Hall/CRC data science series
504			_aContains bibliographical references (pages 391-394) and index
505	0	0	_tBasic probability models -- _tMonte Carlo simulation -- _tDiscrete random variables: expected value -- _tDiscrete random variables: variance -- _tDiscrete parametric distribution families -- _tContinuous probability models -- _tStatistics: prologue -- _tFitting continuous models -- _tThe family of normal distributions -- _tIntroduction to statistical inference -- _tMultivariate distributions -- _tThe multivariate normal family of distributions -- _tMixture distributions -- _tMultivariate description and dimension reduction -- _tPredictive modeling -- _tModel parsimony and overfitting -- _tIntroduction to discrete time Markov chains -- _tAppendices: A. R Quick Start -- _tB. Matrix algebra
520			_a"Probability and Statistics for Data Science: Math + R + Data covers "math stat"--distributions, expected value, estimation etc.--but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming." --Amazon.com
650		0	_aProbabilities _vTextbooks
650		0	_aMathematical statistics _vTextbooks
650		0	_aProbabilities _xData processing
650		0	_aMathematical statistics _xData processing
830		0	_aSeries in computer science and data analysis
942			_2ddc _cBK _n0
999			_c595 _d595