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### Auteur Peter C. Bruce

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Affiner la recherchePractical statistics for data scientists / Peter C. Bruce

Titre : Practical statistics for data scientists : 50+ essential concepts using R and Python Type de document : texte imprimĂ© Auteurs : Peter C. Bruce, Auteur Mention d'Ă©dition : Second edition Editeur : Sebastopol, CA : O'Reilly Media AnnĂ©e de publication : 2020 Importance : xvi, 342 pages PrĂ©sentation : illustrations (graph) Format : 24 cm ISBN/ISSN/EAN : 978-1-492-07294-2 Langues : Anglais ( eng)CatĂ©gories : 005 Computer programming, programs, data Mots-clĂ©s : Practical statistics for data scientists Index. dĂ©cimale : 005.13 RĂ©sumĂ© : Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning Practical statistics for data scientists [texte imprimĂ©] : 50+ essential concepts using R and Python / Peter C. Bruce, Auteur . - Second edition . - Sebastopol, CA : O'Reilly Media, 2020 . - xvi, 342 pages : illustrations (graph) ; 24 cm.ISBN: 978-1-492-07294-2

Langues : Anglais (eng)

CatĂ©gories : 005 Computer programming, programs, data Mots-clĂ©s : Practical statistics for data scientists Index. dĂ©cimale : 005.13 RĂ©sumĂ© : Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning ## Exemplaires

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