BiteSize Statistics for Absolute Beginners

BiteSize Statistics for Absolute Beginners: Data and Descriptive Statistics is the first course in the BiteSize Stats for Absolute Beginners specialization. It builds the foundational statistical literacy that every later course depends on: how to think like a statistician, how to classify and measure data correctly, and how to summarize a dataset's center, spread, and shape. Across five modules, learners progress from the Prepare-Analyze-Conclude workflow and the DIKW framework, through population/sample vocabulary and the four probability sampling methods, to the three measures of center (mean, median, mode), the four measures of spread (range, IQR, variance/SD, CV), and finally frequency tables, histograms, distribution shape, the Empirical Rule, and box plots. Every core lesson pairs a short video walkthrough and reading with a hands-on interactive notebook built around a realistic business scenario, and each module closes with a graded applied lab using a real dataset.

BiteSize Statistics for Absolute Beginners: Key Probability Distributions is the third and final course in the BiteSize Stats for Absolute Beginners specialization. It shows how the probability rules from Course 2 turn into named, reusable models — random variables and their distributions, the binomial and Poisson families for discrete counts, the Normal distribution for continuous measurements, and the sampling distributions that connect any of them back to real data. Across five modules, learners progress from random variables, PMFs, expected value, and CDFs, through the binomial distribution for fixed-trial counts and the Poisson distribution for rate-based counts, to the Normal distribution, Z-scores, and inverse-Normal problems, and finally the Central Limit Theorem and sampling distributions of the mean and proportion. Every core lesson pairs a short video walkthrough and reading with a hands-on interactive notebook built around a realistic business scenario, and each module closes with a graded applied lab using a real dataset; two modules also include a fully worked bonus case study.