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 :	ill. en coul, couv. ill. en coul
Format :	24 cm
ISBN/ISSN/EAN :	978-3-030-10530-3
Langues :	Anglais (eng)
Mots-clés :	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 :	519.2 Probabilités et statistiques mathématiques
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.) : ill. en coul, couv. ill. en coul ; 24 cm. - (Undergraduate topics in computer science, ISSN 2197-1781) .
ISBN : 978-3-030-10530-3
Langues : Anglais (eng)

Mots-clés :	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 :	519.2 Probabilités et statistiques mathématiques
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