03331nam a22004335i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001500153072001600168072002300184072001500207100008500222245017900307250001800486264005400504300006900558336002600627337002600653338003600679347002400715505033000739520143001069650005002499650004602549650002202595650002602617650001802643650002202661710003402683773002602717776003602743776003602779776003602815856004602851979-8-8688-0348-2DE-He21320251030112616.0cr nn 008mamaa240705s2024    xxu|    s    |||| 0|eng d  a97988688034827 a10.1007/979-8-8688-0348-22doi 4aQA76.7-.73 7aUMX2bicssc 7aCOM0510102bisacsh 7aUMX2thema1 aVillalobos Alva, Jalil.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aBeginning Mathematica and Wolfram for Data Scienceh[electronic resource] :bApplications in Data Analysis, Machine Learning, and Neural Networks /cby Jalil Villalobos Alva.  a2nd ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXXIII, 462 p. 368 illus., 308 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 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.  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. . 0aProgramming languages (Electronic computers). 0aArtificial intelligencexData processing. 0aMachine learning.14aProgramming Language.24aData Science.24aMachine Learning.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886880347508iPrinted edition:z979886880349908iPrinted edition:z979886881089340uhttps://doi.org/10.1007/979-8-8688-0348-2