000			a
999			_c30648 _d30648
008			220309b xxu||||| |||| 00| 0 eng d
020			_a9783030105303
082			_a519.50285 _bKAP
100			_aKaptein, Maurits
245			_aStatistics for data scientists : an introduction to probability, statistics, and data analysis
260			_bSpringer, _c2022 _aCham :
300			_axxiv, 321 p. ; _bill., _c23 cm
365			_b49.99 _cEUR _d84.70
490			_aUndergraduate topics in computer science
504			_aIncludes bibliographical references.
520			_aThis book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis supported by numerous real data examples and reusable [R] code with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.
650			_aMathematical analysis
650			_aQuantitative Social Science
650			_aComputer science and Mathematics
650			_aStatistical methods
650			_aMathematical statistics
650			_aProbabilities
650			_aDistribution function
650			_a Estimator
650			_aLognormal
650			_a Null hypothesis
650			_aPearson's plot
650			_aRandom variables
650			_aSampling
650			_a Simple random statistics
650			_a Bootstrapping
650			_a Bayesian
650			_a Standard deviation
650			_aData visualisation
700			_aHeuvel, Edwin van den
942			_2ddc _cBK