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003			OSt
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008			210909t20202020caua e b 001 0 eng
020			_a9788194435006
040			_cSTCPL
041			_aENG
082	0	4	_a519.5 _bPET-P
100	1		_aBruce, Peter C.
100	1		_aBruce, Andrew
100	1		_aGedeck, Peter
245	1	0	_aPractical statistics for data scientists : _b50+ essential concepts using R and Python / _cby Peter Bruce, Andrew Bruce and Peter Gedeck
250			_a2nd ed.
260			_aSebastopol: _bO'Reilly Media Inc. , _c2020.
300			_axvi, 342 p. _bPB
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.--
650		0	_aCommerce
650		0	_aStatistics
650		0	_aMathematical analysis
650		0	_aQuantitative research
650		0	_aR (Computer program language)
650		0	_aPython (Computer program language)
650		0	_aStatistical methods
650		7	_aData processing
942			_cBK
942			_2ddc
942			_2ddc
999			_c87008 _d87008