Essential Statistics for Data Science · Part 2 of 4
Before any model is fit, the first job is to understand the data, its center, its spread, its shape, and the outliers that change the story. This is the work descriptive statistics does, and skipping it is how production models fail in ways their training metrics never warned you about.
Part 2 walks through mean / median / mode, variance / standard deviation / IQR, skewness and kurtosis, and how to translate each into a real interpretation of what's going on in the dataset, with the data-science workflows where each one actually changes a decision.