Free online courseApplied Statistics

Course Description

Applied Statistics gives you the tools to make sense of data and turn numbers into clear, defensible conclusions. Whether you are preparing for school assessments or want stronger analytical skills for work, this free online course helps you move beyond memorizing formulas and toward understanding what results mean in real situations.

You will start by learning how statisticians describe and classify data, how studies are designed, and why sampling methods matter. From there, you will get comfortable organizing information using frequency distributions and visual displays, then learn to spot common ways graphs can mislead. As you practice measures of center and spread, you will see how choices like mean versus median can change the story, especially when outliers or skewed distributions appear.

Probability becomes a practical decision-making toolkit as you learn the addition and multiplication rules, as well as counting methods like permutations and combinations. Those ideas connect directly to widely used probability models, helping you recognize when a situation fits binomial, Poisson, or hypergeometric reasoning and how expected value and variance describe outcomes over time.

The course then builds toward inference, where many learners feel statistics becomes truly useful. You will work with normal distributions and z-scores, understand what area under the curve represents, and use the central limit theorem to justify why sample results can be generalized. Confidence intervals and hypothesis tests are explained as structured ways to estimate and evaluate claims, including cases with known or unknown variation, comparisons between two groups, and paired data.

Finally, you will connect statistics to relationships in data through correlation and regression, learning how models can support prediction and how to interpret measures like R-squared responsibly. Throughout, targeted exercises reinforce each idea so you develop accuracy, intuition, and the confidence to critique studies, communicate findings, and apply statistical thinking in everyday and academic contexts.