Statistical Modeling and Inference for Data Science

Catalog description

Covers the basic statistical concepts underlying data science as well as some of the major methods. Includes fundamental ideas such as the key idea in predictive modeling is the bias-variance tradeoff, and cross validation is the basic approach for dealing with the bias-variance tradeoff. Statistical inference underlies much of data science methodology. Includes Bayesian and frequentist approaches to inference and how they are used in some of key ideas and methods in data science such as causal inference with observational data. Covers some key methods such as K nearest neighbors, naive Bayes classification, A/B testing, linear models, Gaussian processes and data reduction.