Overview
- Introduces statistics and data science students to classical and modern statistical concepts
- Features detailed derivations and explanations of complex statistical methods
- Includes statistical tools for applied data science, e.g. for missing data or causality
Part of the book series: Springer Series in Statistics (SSS)
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About this book
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.
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Keywords
Table of contents (12 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
Göran Kauermann is a Professor of Statistics at the Department of Statistics and Chair of the Elite Master’s Program in Data Science at the LMU Munich, Germany. He is a recognized expert in applied statistics. He previously served as Editor-in-Chief of AStA Advances in Statistical Analysis, a journal of the German Statistical Society.
Helmut Küchenhoff is a Professor of Statistics at the Department of Statistics and Head of the Statistical Consulting Unit (StaBLab) at the LMU Munich, Germany. He has extensive experience in working on practical statistical projects in science and industry. His teaching focuses on practical work, where students engage in practical projects with real-world problems.
Christian Heumann is a Professor at the Department of Statistics, LMU Munich, Germany, where he teaches students in both the Bachelor’s and Master’s programs. His research interests include statistical modeling, computational statistics and methods for missing data, also in connection with causal inference. Recently, he has begun exploring statistical methods in natural language processing.
Bibliographic Information
Book Title: Statistical Foundations, Reasoning and Inference
Book Subtitle: For Science and Data Science
Authors: Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-3-030-69827-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-69826-3Published: 01 October 2021
Softcover ISBN: 978-3-030-69829-4Published: 02 October 2022
eBook ISBN: 978-3-030-69827-0Published: 30 September 2021
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XIII, 356
Number of Illustrations: 77 b/w illustrations, 10 illustrations in colour
Topics: Statistical Theory and Methods, Data Structures and Information Theory, Artificial Intelligence, Data Mining and Knowledge Discovery, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences