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Modeling: Regression and Beyond
Linear regression with assumptions, logistic regression and the GLM family, Bayesian inference, and a causal-inference primer (confounders, DAGs).
Module Content
Linear Regression — The Math, Not Just the API
What `LinearRegression().fit()` actually does, and the assumptions that make it valid. FIND_VIDEO: search 'linear regression OLS assumptions' — recommended channel: StatQuest / 3Blue1Brown. Aim for 11 min or under.
QUIZ: OLS, Assumptions, and When They Break
Practice quiz: OLS is everywhere. The four assumptions are what determine whether your coefficients mean anything.
Logistic Regression and the GLM Family
The link between linear regression and binary classification. Plus the GLM framework that generalizes to counts, proportions, durations. FIND_VIDEO: search 'logistic regression GLM tutorial' — recommended channel: StatQuest. Aim for 11 min or under.
QUIZ: From Linear to Logistic, and Why
Practice quiz: Logistic regression isn't 'regression on binary data'. It's a different model with a different likelihood. Understanding the difference is what unlocks GLMs.
Bayesian Inference — The Update Game
Stop hiding from Bayes. Prior + likelihood = posterior. The update rule that powers modern probabilistic modeling. FIND_VIDEO: search 'Bayesian inference prior posterior' — recommended channel: 3Blue1Brown / StatQuest. Aim for 11 min or under.
QUIZ: Priors, Likelihoods, Posteriors in Practice
Practice quiz: The Bayesian framework in three formulas. Where it shines, where it doesn't, and the conjugate trick.
Causal Inference Primer — Beyond Correlation
The single biggest leap from analytics to data science. Correlation ≠ causation, and how to reason about WHY. FIND_VIDEO: search 'causal inference confounders DAG' — recommended channel: StatQuest / 3Blue1Brown. Aim for 11 min or under.
QUIZ: Confounders, DAGs, and the 'Why' of Causal
Practice quiz: Causal inference is its own field. The basics: confounders, DAGs, the difference between association and causation, and the tools that bridge the two.
SUBMISSION: Project 4 — Multivariable Regression with Multicollinearity Diagnostics (VIF) & Interaction Terms
Explain churn with an OLS model on 50K customers: diagnose multicollinearity with VIF, test tenure × plan interactions, validate assumptions with residual diagnostics, and defend the model to a skeptical PM.