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| 037 |
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_aQA76.9.D343 _bL376 2019 |
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_a006.3/12 _223 |
| 049 | _aMAIN | ||
| 100 | 1 |
_aLarose, Chantal D., _eauthor. _98187 |
|
| 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 |
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| 337 |
_acomputer _bn _2rdamedia |
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| 338 |
_aonline resource _bnc _2rdacarrier |
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| 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. _93907 |
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_aPython (Computer program language) _96666 |
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_aR (Computer program language) _94991 |
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| 650 | 0 |
_aBig data. _94174 |
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_aData structures (Computer science) _98188 |
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_aCOMPUTERS _xGeneral. _2bisacsh _94629 |
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| 650 | 7 |
_aBig data. _2fast _0(OCoLC)fst01892965 _94174 |
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| 650 | 7 |
_aData mining. _2fast _0(OCoLC)fst00887946 _93907 |
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| 650 | 7 |
_aData structures (Computer science) _2fast _0(OCoLC)fst00887978 _98188 |
|
| 650 | 7 |
_aPython (Computer program language) _2fast _0(OCoLC)fst01084736 _96666 |
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| 650 | 7 |
_aR (Computer program language) _2fast _0(OCoLC)fst01086207 _94991 |
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_aElectronic books. _93294 |
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_aLarose, Daniel T., _eauthor. _98189 |
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_iPrint version: _aLarose, Chantal D. _tData science using Python and R. _dHoboken, NJ : John Wiley & Sons, Inc, 2019 _z9781119526810 _w(DLC) 2019007280 |
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_uhttps://doi.org/10.1002/9781119526865 _zWiley Online Library |
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