| 000 | 03281nam a2200541 i 4500 | ||
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| 001 | AH28571892 | ||
| 003 | StDuBDS | ||
| 005 | 20241009133925.0 | ||
| 007 | cr|||||||||||| | ||
| 008 | 150427s2015 cc a fs 001|0|eng|d | ||
| 010 | _a2015472852 | ||
| 020 | _a9781491904404 (e-book) | ||
| 040 |
_aStDuBDS _beng _cStDuBDS _dStDuBDSZ _erda _dUkPrAHLS |
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| 050 | 4 | _aQA76.9.D343 | |
| 072 | 7 |
_aCOM _2ukslc |
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| 072 | 7 |
_aUY _2bicssc |
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| 072 | 7 |
_aUY _2thema |
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| 072 | 7 |
_aUN _2thema |
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| 072 | 7 |
_aUNA _2thema |
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| 072 | 7 |
_aUYZM _2thema |
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| 082 | 0 | 4 | _223 |
| 100 | 1 |
_aGrus, Joel, _eauthor. |
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| 245 | 1 | 0 |
_aData science from scratch _h[electronic resource] / _cJoel Grus. |
| 250 | _aFirst edition. | ||
| 264 | 1 |
_aBeijing : _bO'Reilly, _c2015. |
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| 300 |
_axvi, 311 pages : _billustrations (black and white) |
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| 336 |
_atext _2rdacontent |
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| 336 |
_astill image _2rdacontent |
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| 337 |
_acomputer _2rdamedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 366 | _b20150501 | ||
| 500 | _aIncludes QR code. | ||
| 500 | _aIncludes index. | ||
| 520 | 8 |
_aData science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. _bData science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in Python Learn the basics of linear algebra, statistics, and probability-and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
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| 530 | _aAlso available in printed form ISBN 9781491901427 | ||
| 533 |
_aElectronic reproduction. _cAskews and Holts. _nMode of access: World Wide Web. |
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| 650 | 0 | _aData mining. | |
| 650 | 0 |
_aData mining _xMathematics. |
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| 650 | 0 | _aPython (Computer program language) | |
| 650 | 7 |
_aComputers and IT. _2ukslc |
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| 650 | 7 |
_aComputer science _2thema |
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| 650 | 7 |
_aDatabases _2thema |
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| 650 | 7 |
_aDatabase design & theory _2thema |
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| 650 | 7 |
_aInformation architecture _2thema |
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| 655 | 7 |
_aElectronic books. _2lcsh |
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| 856 | 4 | 0 |
_uhttp://www.vlebooks.com/vleweb/product/openreader?id=BradfordC&isbn=9781491904404 _zClick here to access |
| 710 | _aVLeBooks | ||
| 999 |
_c81890 _d81890 |
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