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.
Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university”s Distinguished Teaching Award.
หนังสืออิเล็กทรอนิกส์เพิ่มเติมจากผู้แต่งคนเดียวกัน / บรรณาธิการ
48,995 หนังสืออิเล็กทรอนิกส์ในหมวดหมู่นี้
