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024 7 _a10.1007/979-8-8688-0348-2
_2doi
035 _a(OCoLC)1444158265
037 _a9798868803482
_bO'Reilly Media
040 _aORMDA
_beng
_erda
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050 4 _aQA76.73.M29
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072 7 _aCOM051010
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072 7 _aUMX
_2thema
082 0 4 _a510.285/536
_223/eng/20240710
100 1 _aVillalobos Alva, Jalil,
_eauthor
245 1 0 _aBeginning Mathematical and Wolfram for data science :
_bapplications in data analysis, machine learning, and neural networks /
_cJalil Villalobos Alva
250 _a2nd ed
264 1 _aNew York, NY :
_bApress,
_c[2024]
300 _a1 online resource (xxiii, 462 pages) :
_bchiefly illustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
340 _gpolychrome
_2rdacc
_0http://rdaregistry.info/termList/RDAColourContent/1003
500 _aIncludes index
505 0 _a1. Introduction to Mathematica -- 2. Data Manipulation -- 3. Working with Data and Datasets -- 4. Import and Export -- 5. Data Visualization -- 6. Statistical Data Analysis -- 7. Data Exploration -- 8. Machine Learning with the Wolfram Language -- 9. Neural Networks with the Wolfram Language -- 10. Neural Network Framework
506 _aAvailable to OhioLINK libraries
520 _aEnhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You'll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You'll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you'll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language
650 0 _aMathematica (Computer program language)
_0https://id.loc.gov/authorities/subjects/sh93005423
650 0 _aWolfram language (Computer program language)
_0https://id.loc.gov/authorities/subjects/sh2015002436
650 0 _aMathematics
_xData processing.
_0https://id.loc.gov/authorities/subjects/sh85082146
650 0 _aArtificial intelligence.
_0https://id.loc.gov/authorities/subjects/sh85008180
710 2 _aOhio Library and Information Network.
_0https://id.loc.gov/authorities/names/no95058981
856 4 0 _3OhioLINK
_zConnect to resource
_uhttps://rave.ohiolink.edu/ebooks/ebc2/9798868803482
856 4 0 _3SpringerLink
_zConnect to resource
_uhttps://link.springer.com/10.1007/979-8-8688-0348-2
856 4 0 _3SpringerLink
_zConnect to resource (off-campus)
_uhttps://go.ohiolink.edu/goto?url=https://link.springer.com/10.1007/979-8-8688-0348-2
856 4 0 _3O'Reilly
_zConnect to resource
_uhttps://learning.oreilly.com/library/view/~/9798868803482/?ar
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