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.
| ISBN: | 9783030698294 |
| Publication date: | 2nd October 2022 |
| Author: | Göran Kauermann, Helmut Küchenhoff, Christian Heumann |
| Publisher: | Springer Nature Switzerland AG |
| Format: | Paperback |
| Pagination: | 356 pages |
| Series: | Springer Series in Statistics |
| Genres: |
Probability and statistics Algorithms and data structures Information theory Artificial intelligence Data mining Expert systems / knowledge-based systems |
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.
Statistical Foundations, Reasoning and Inference features in the following genres: Probability and statistics, Algorithms and data structures, Information theory, Artificial intelligence, Data mining, Expert systems / knowledge-based systems
Statistical Foundations, Reasoning and Inference is available in Paperback
Statistical Foundations, Reasoning and Inference was written by Göran Kauermann, Helmut Küchenhoff, Christian Heumann and published by Springer Nature Switzerland AG
Statistical Foundations, Reasoning and Inference has 356 pages
Yes it is part of Springer Series in Statistics series
£67.49