000			03353cam a2200409 i 4500
015			_aGBB956595 _2bnb
016	7		_a019327510 _2Uk
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)
020			_z9781119526810 _q(hardcover)
037			_a9781119526841 _bWiley
040			_cCUS
072		7	_aCOM _x000000 _2bisacsh
100	1		_aLarose, Chantal D., _eauthor.
245	1	0	_aData science using Python and R / _cChantal D. Larose, Daniel T. Larose.
260		1	_aHoboken, NJ : _bJohn Wiley & Sons, Inc, _c2019.
260		4	_c©2019
300			_a1 online resource (xvii, 238 pages)
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)
650		7	_aCOMPUTERS _xGeneral. _2bisacsh
650		7	_aBig data. _2fast _0(OCoLC)fst01892965
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aData structures (Computer science) _2fast _0(OCoLC)fst00887978
650		7	_aPython (Computer program language) _2fast _0(OCoLC)fst01084736
650		7	_aR (Computer program language) _2fast _0(OCoLC)fst01086207
700	1		_aLarose, Daniel T., _eauthor.
856	4	0	_uhttps://doi.org/10.1002/9781119526865 _zWiley Online Library
942			_cEBK
999			_c208843 _d208843