Statistical Foundations, Reasoning and Inference For Science and Data Science |
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| Author:
| Kauermann, Göran
Küchenhoff, Helmut
Heumann, Christian |
| Series title: | Springer Series in Statistics Ser. |
| ISBN: | 978-3-030-69829-4 |
| Publication Date: | Oct 2022 |
| Publisher: | Springer International Publishing AG
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| Imprint: | Springer |
| Book Format: | Paperback |
| List Price: | USD $99.99USD $89.99 |
Book Description:
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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,...
More Description
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.