Statistical Foundations, Reasoning and Inference For Science and Data Science

You must be logged in to access this title.

Sign up now

Already a member? Log in

Synopsis

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.

Book details

Edition:
1st ed. 2021
Series:
Springer Series in Statistics
Author:
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
ISBN:
9783030698270
Related ISBNs:
9783030698263
Publisher:
Springer International Publishing
Pages:
N/A
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2021-11-01
Usage restrictions:
Copyright
Copyright date:
2021
Copyright by:
The Editor 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Mathematics and Statistics, Nonfiction