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020			_a9781119526841 _q(electronic book)
020			_a1119526841 _q(electronic book)
020			_a9781119526834 _q(electronic book)
020			_a1119526833 _q(electronic book)
020			_a9781119526865 _q(electronic book)
020			_a1119526868 _q(electronic book)
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037			_a9781119526841 _bWiley
040			_aDLC _beng _erda _epn _cDLC _dOCLCO _dNST _dYDX _dEBLCP _dDG1 _dYDX _dUKMGB _dRECBK _dUKAHL _dOCLCF _dOCLCQ _dCOO
042			_apcc
049			_aMAIN
090			_aQA76.9.D343 _bL376 2019
100	1		_aLarose, Chantal D., _eauthor.
245	1	0	_aData science using Python and R / _cChantal D. Larose, Daniel T. Larose.
264		1	_aHoboken, NJ : _bJohn Wiley & Sons, Inc, _c2019.
264		4	_c©2019
300			_a1 online resource (xvii, 238 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
588	0		_aOnline resource; title from digital title page (viewed on April 03, 2019).
520			_aLearn data science by doing data science! Data Science Using Python and R will get you plugged into the world's two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist'the hottest job in America.' Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naIve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.
650		0	_aData mining.
650		0	_aPython (Computer program language)
650		0	_aR (Computer program language)
650		0	_aBig data.
650		0	_aData structures (Computer science)
655		0	_aElectronic books.
700	1		_aLarose, Daniel T., _eauthor.
773	0		_a _tWiley e-books
776	0	8	_iPrint version: _aLarose, Chantal D. _tData science using Python and R. _dHoboken, NJ : John Wiley & Sons, Inc, 2019 _z9781119526810 _w(DLC) 2019007280
856	4	0	_uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=https://doi.org/10.1002/9781119526865 _zWiley Online Library
907			_a.b16817126 _b2022-10-05 _c2020-07-17
942			_n0 _kQA76.9.D343 L376 2019
998			_ae _b2020-07-17 _cm _dz _feng _gnju _y0 _z.b16817126
999			_c649121 _d649121