TY  - BOOK
AU  - Matloff,Norman S.
TI  - Probability and statistics for data science: math + R + data 
T2  - Chapman & Hall/CRC data science series
SN  - 9781138393295
AV  - QA273 .M38495 2020
U1  -  519.5 MAT 2020 
PY  - 2020///]
CY  - Boca Raton
PB  - CRC Press, Taylor & Francis Group
KW  - Probabilities
KW  - Textbooks
KW  - Mathematical statistics
KW  - Data processing
N1  - Contains bibliographical references (pages 391-394) and index; Basic probability models --; Monte Carlo simulation --; Discrete random variables: expected value --; Discrete random variables: variance --; Discrete parametric distribution families --; Continuous probability models --; Statistics: prologue --; Fitting continuous models --; The family of normal distributions --; Introduction to statistical inference --; Multivariate distributions --; The multivariate normal family of distributions --; Mixture distributions --; Multivariate description and dimension reduction --; Predictive modeling --; Model parsimony and overfitting --; Introduction to discrete time Markov chains --; Appendices: A. R Quick Start --; B. Matrix algebra
N2  - "Probability and Statistics for Data Science: Math + R + Data covers "math stat"--distributions, expected value, estimation etc.--but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming." --Amazon.com
ER  -