Welcome to Statistics Fundamentals, the first module of the Statistics & Probability course. Before you can test a hypothesis or train a model, you need to do something deceptively simple well: describe what your data actually looks like. That is what this module is about.

You will start with the single most important distinction in the field — the difference between a population and a sample — and learn why almost all real analysis works with samples and what can go wrong when they are not representative. From there you will learn to classify variables by their measurement scale, summarize them with frequency distributions, and read their shape from histograms and bar charts.

Every idea is grounded in one real dataset: physical measurements of 344 penguins from three species in the Palmer Archipelago, Antarctica. You will load it with pandas, sample from it, tabulate it, and chart it — so that by the end of the module the words “skewed,” “representative,” and “distribution” mean something concrete you can see.

Start with Lesson 1, where you will meet populations, samples, and the sampling methods that keep your conclusions honest.