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Live DS Statistical Cases
Six interview-grade walkthroughs: diagnosing a 'significant' A/B test that isn't, bootstrap CIs for skewed metrics, fitting and interpreting churn logistic, power analysis, confounder detection, and Bayesian product reasoning.
Module Content
SUBMISSION: Capstone 1 — Quasi-Experimentation with Difference-in-Differences (DiD) on a Product Launch
Estimate the causal impact of a staggered city-wise feature rollout without randomization: build the DiD estimator, defend parallel trends with pre-period event-study plots, run placebo/falsification checks, and deliver a launch recommendation.
SUBMISSION: Capstone 2 — Bayesian A/B Testing Engine with Conjugate Beta-Binomial Updating
Build a reusable Bayesian A/B engine: Beta-Binomial conjugate updating, posterior P(B>A), expected-loss ship rules, prior-sensitivity analysis, and a live decision dashboard for the onboarding-flow split.