ISBN: 9783030698263 - statistical foundations, reasoning and inference: for science and data science (springer series in statistics) (15 Ergebnisse)

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Hardcover. Zustand: Brand New. 369 pages. 9.25x6.10x1.10 inches. In Stock.

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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills. 372 pp. Englisch.…

Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces statistics and data science students to classical and modern statistical conceptsFeatures detailed derivations and explanations of complex statistical methodsIncludes statistical tools for applied data science, e.g. for missing d.…

Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills. …

Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master¿s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 372 pp. Englisch. …

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