Learning path
Work through the starter material first, then move into core econometric concepts and data interpretation.
Start here
Choose the simplest Python path for your device, coursework, and goals. Installation is optional when browser Python or Colab is enough.
Module
A practical bridge into econometrics with beginner-friendly notation, summary statistics, uncertainty, and Python data workflows.
Module 1
Core ideas for thinking like an applied econometrician: models, variables, error terms, data structures, and interpretation.
Module 2
Estimate and interpret one-variable regression models with fitted lines, residuals, R-squared, Python output, and practice projects.
Module 3
Learn how multiple regression estimates partial relationships, controls for observed differences, diagnoses omitted-variable bias and collinearity, and supports responsible Python analysis.
Module 4
Learn how to make statistical inferences in multiple regression using t tests, p-values, confidence intervals, linear restrictions, F tests, and professional regression reporting.
Module 5
Learn how OLS behaves as sample size grows, why consistency matters, how large-sample inference works without normal errors, and how to use simulations and Python to understand asymptotic normality, asymptotic standard errors, LM tests, and asymptotic efficiency.
Module 6
Extend multiple regression beyond the linear-in-levels baseline by learning how scaling, logs, quadratics, interactions, adjusted R-squared, prediction, and careful controls change interpretation.
Module 7
Learn how to use qualitative information in regression with binary variables, categorical indicators, interactions, group-specific slopes, treatment evaluation, and linear probability models.
Module 8
Learn how changing error variance affects regression inference, how to use heteroskedasticity-robust standard errors and tests, how to diagnose heteroskedasticity, and when weighted least squares or feasible GLS may improve estimation.
Flagship course
A complete Python-first pathway from programming foundations to econometrics, causal inference, forecasting, financial risk, machine learning, and responsible AI.