Statistics for data scientists / Maurits Kaptein / 978-3-030-10530-3

Public ISBD Titre : Statistics for data scientists : an introduction to probability, statistics, and data analysis Type de document : texte imprimé Auteurs : Maurits Kaptein (19..-..), Auteur ; Edwin Van den Heuvel (19..-..), Auteur Editeur : Cham : Springer Collection : Undergraduate topics in computer science, ISSN 2197-1781 Importance : 1 vol. (xxiv-321 p.) Présentation : couv. ill. en coul Format : 24 cm ISBN/ISSN/EAN : 978-3-030-10530-3 Langues : Anglais (eng) Catégories : 51 Mathématiques :519.2 Probabilité. Statistique mathématique Tags : Statistical methods  Data analysis  Inferential statistics  Exploratory data analysis  Regression analysis  Machine learning  Statistical modeling  Probability theory Hypothesis testing  Statistical computing  Data visualization  Predictive analytics  Statistical learning  R programming  Python for statistics. Index. décimale : 519.23 Résumé : This provides an undergraduate introduction to analysing data for science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis - supported by numerous real data examples and reusable [R] code - with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern mehtods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for date science Statistics for data scientists : an introduction to probability, statistics, and data analysis [texte imprimé] / Maurits Kaptein (19..-..), Auteur ; Edwin Van den Heuvel (19..-..), Auteur . - Cham : Springer, [s.d.] . - 1 vol. (xxiv-321 p.) : couv. ill. en coul ; 24 cm. - (Undergraduate topics in computer science, ISSN 2197-1781) . ISBN : 978-3-030-10530-3 Langues : Anglais (eng) Catégories : 51 Mathématiques :519.2 Probabilité. Statistique mathématique Tags : Statistical methods  Data analysis  Inferential statistics  Exploratory data analysis  Regression analysis  Machine learning  Statistical modeling  Probability theory Hypothesis testing  Statistical computing  Data visualization  Predictive analytics  Statistical learning  R programming  Python for statistics. Index. décimale : 519.23 Résumé : This provides an undergraduate introduction to analysing data for science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis - supported by numerous real data examples and reusable [R] code - with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern mehtods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for date science

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