English | 2020 | ISBN: 0367260934 | 444 Pages | PDF | 10 MB
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.