Probability and statistics for data science : math + R + data /
Matloff, Norman S.,
Probability and statistics for data science : math + R + data / Norman Matloff - xxxii, 412 pages : illustrations ; 24 cm - Chapman & Hall/CRC data science series . - Series in computer science and data analysis .
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
"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
9781138393295 9781138393295 9781138393295 036726093X
16051385
2019008196
GBB9B5674 bnb
Probabilities--Textbooks
Mathematical statistics--Textbooks
Probabilities--Data processing
Mathematical statistics--Data processing
QA273 / .M38495 2020
519.5 MAT 2020
Probability and statistics for data science : math + R + data / Norman Matloff - xxxii, 412 pages : illustrations ; 24 cm - Chapman & Hall/CRC data science series . - Series in computer science and data analysis .
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
"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
9781138393295 9781138393295 9781138393295 036726093X
16051385
2019008196
GBB9B5674 bnb
Probabilities--Textbooks
Mathematical statistics--Textbooks
Probabilities--Data processing
Mathematical statistics--Data processing
QA273 / .M38495 2020
519.5 MAT 2020