Exploratory Data Analysis: estimates of locations and variability, exploring data distributions, exploring binary and categorical data, exploring two or more variables.

Data and Sampling Distributions: Random sampling and bias, selection bias, sampling distribution of statistic, bootstrap, confidence intervals, data distributions: normal, long tailed, student’s-t, binomial, Chi-square, F distribution, Poisson and related distributions.

Statistical Experiments and Significance Testing: A/B testing, hypothesis testing, resampling, statistical significance & p-values, t-tests, multiple testing, degrees of freedom.

Multi-arm bandit algorithm power and sample size, factor variables in regression, interpreting the regression equation, Regression diagnostics, Polynomial and Spline Regression.

Discriminant Analysis: Covariance Matrix, Fisher’s Linear discriminant, Generalized Linear Models, Interpreting the coefficients and odd ratios, Strategies for Imbalanced Data.