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008			210909t20202020caua e b 001 0 eng
010			_a 2018420845
015			_aGBC061788 _2bnb
016	7		_a019800669 _2Uk
020			_a9781492072942 _q(paperback)
020			_a149207294X _q(paperback)
035			_a(OCoLC)on1158315601
040			_aUTV _beng _cUTV _erda _dUTV _dAHH _dOCLCF _dYDXIT _dUKMGB _dIBI _dOCLCO _dYDX _dJAS _dOCL _dDLC _dJO-AjAnu
041			_aeng _heng
042			_alccopycat
050	0	0	_aQA276.4 _b.B78 2020
082	0	4	_a001.4/22 _223
100	1		_aBruce, Peter C., _d1953- _eauthor.
245	1	0	_aPractical statistics for data scientists : _b50+ essential concepts using R and Python / _cPeter Bruce, Andrew Bruce, and Peter Gedeck.
250			_aSecond edition.
264		1	_aSebastopol, CA : _bO'Reilly Media, Inc., _c2020.
264		4	_c©2020
300			_axvi, 342 pages : _billustrations ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references (pages 327-328) and index.
505	0		_aExploratory Data Analysis -- Data and Sampling Distributions -- Statistical Experiments and Significance Testing -- Regression and Prediction -- Classification -- Statistical Machine Learning -- Unsupervised Learning.
520			_aStatistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.-- _cSource other than the Library of Congress.
650		0	_aMathematical analysis _xStatistical methods.
650		0	_aQuantitative research _xStatistical methods.
650		0	_aR (Computer program language)
650		0	_aPython (Computer program language)
650		0	_aStatistics _xData processing.
650		7	_aPython (Computer program language) _2fast _0(OCoLC)fst01084736
650		7	_aR (Computer program language) _2fast _0(OCoLC)fst01086207
650		7	_aStatistics _xData processing. _2fast _0(OCoLC)fst01132113
700	1		_aBruce, Andrew, _d1958- _eauthor.
700	1		_aGedeck, Peter, _eauthor.
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c33527 _d33527