000			a
999			_c32530 _d32530
008			230901b xxu||||| |||| 00| 0 eng d
020			_a9783030698294
082			_a519.5 _bKAU
100			_aKauermann, Goran
245			_aStatistical foundations, reasoning and inference : for science and data science
260			_bSpringer, _c2021 _aCham :
300			_axiii, 356 p. ; _bill., _c24 cm
365			_b79.99 _cEUR _d94.90
490			_aSpringer series in statistics
504			_aIncludes bibliographical references and index.
520			_aThis 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.
650			_aMathematical statistics
650			_aData structures
650			_aData mining
650			_aArtificial intelligence
700			_aKuchenhoff, Helmut
700			_aHeumann, Christian
942			_2ddc _cBK