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Probability and statistics for data science : math + R + data / Norman Matloff

By: Material type: TextTextSeries: Series in computer science and data analysisPublisher: Boca Raton : CRC Press, Taylor & Francis Group, [2020]Copyright date: ©2020Description: xxxii, 412 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781138393295
  • 9781138393295
  • 9781138393295
  • 036726093X
Subject(s): DDC classification:
  • 519.5 MAT 2020
LOC classification:
  • QA273 .M38495 2020
Contents:
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
Summary: "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
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Book Book Symbiosis International University, Dubai 519.5 MAT 2020 (Browse shelf(Opens below)) 1 Available SIU01051

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

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