- Part 1: Statistics. Provides a gentle introduction to the field of statistics, the relationship to machine learning, and the importance that statistical methods have when working through a predictive modeling problem.
- Part 2: Foundation. Introduction to descriptive statistics, data visualization, random numbers, and important findings in statistics such as the law of large numbers and the central limit theorem.
- Part 3: Hypothesis Testing. Covers statistical hypothesis tests for comparing popula- tions of samples and the interpretation of tests with p-values and critical values.
- Part 4: Resampling. Covers methods from statistics used to economically use small samples of data to evaluate predictive models such as k-fold cross-validation and the bootstrap.
- Part 5: Estimation Statistics. Covers an alternative to hypothesis testing called estimation statistics, including tolerance intervals, confidence intervals, and prediction intervals.
- Part 6: Nonparametric Methods. Covers nonparametric statistical hypothesis testing methods for use when data does not meet the expectations of parametric tests.