Design experiments that survive real scrutiny.
Go from causal question to production experiment with hypotheses, metrics, sample size, randomization, diagnostics, CUPED, sequential testing, and bandits.
Turn a product question into a falsifiable hypothesis
Define primary, secondary, and guardrail metrics
Plan sample size and detect randomization failures
Apply variance reduction and sequential methods responsibly
Bean & Brew, where a free-shipping experiment grows into a complete experimentation program.
Basic statistics and comfort reading Python. The course rebuilds the experiment-specific foundations it uses.
Open any module to review its exact sections. Progress and completion follow you through the course.
Topics include Correlation vs causation, The counterfactual, Random assignment, and more.
Topics include Hypothesis writing, OEC primary metric, Secondary metrics, and more.
Topics include Null hypothesis, p-values, Confidence intervals, and more.
Topics include Statistical power, Effect size (MDE), Sample size formulas, and more.
Topics include Random assignment, Deterministic hashing, Sticky vs session-level, and more.
Topics include Peeking problem, Multiple testing, Simpson's paradox, and more.
Topics include Variance is the enemy, CUPED, Stratification, and more.
Topics include Multi-armed bandits, Switchback experiments, Bayesian A/B, and more.
Product and data analysts
Data scientists responsible for experimentation
PMs and engineers who review test results
These are independent LDS courses that extend the closest skills. Choose the direction that matches what you want to build next.
Build statistical intuition before formulas.
Reason clearly when outcomes are uncertain.
Train models the way working data scientists do.
The first module establishes the language and example used throughout the rest of the course.
