This module covers descriptive statistics, probability distributions, hypothesis testing, p-values, and Pearson correlation — the mathematical language that every machine learning algorithm is written in. Students understand how these concepts directly support model building and evaluation. Practical examples connect statistical theory to real data-driven decision making.