Course Schedule

20 Weeks · 3 Lectures Each · 60 Total

The course runs for 20 weeks. Each week consists of three 50-minute lectures. Weeks 1–6 cover Python fundamentals; Weeks 7–20 cover econometric theory and application.

Phase 1 — Python Foundations (Weeks 1–6)

Week Title Lecture 1 Lecture 2 Lecture 3
1 Python Environment & Basics Getting Started with Python Variables & Data Types Operators & Expressions
2 Control Flow & Functions Conditionals & Loops Functions & Scope List Comprehensions & Lambda
3 NumPy for Economics Arrays & Vectorisation Linear Algebra with NumPy Statistical Operations
4 Pandas & Data Wrangling Series & DataFrames Merge, Reshape & Groupby Real Agricultural Data
5 Data Visualisation Matplotlib Foundations Seaborn Statistical Charts Interactive Plots
6 Working with Real Data Import, Export & APIs Exploratory Data Analysis Data Cleaning Case Study

Phase 2 — Econometrics (Weeks 7–20)

Week Title Lecture 1 Lecture 2 Lecture 3
7 Introduction to Econometrics What is Econometrics? Economic Data Types Simple OLS Derivation
8 OLS: Theory & Properties Gauss-Markov Theorem Statistical Properties of OLS Python OLS from Scratch
9 Inference in OLS Hypothesis Testing Confidence Intervals t and F Tests in Python
10 Multiple Linear Regression MLR Setup & Estimation Interpretation of Coefficients Partial Effects in Python
11 Multicollinearity Nature & Detection Consequences VIF & Remedies in Python
12 Heteroscedasticity Nature & Consequences Detection Tests WLS & Robust SE in Python
13 Dummy Variables Qualitative Predictors Interaction Terms Structural Breaks in Python
14 Nonlinear Regression Log & Polynomial Models Splines Python Curve Fitting
15 Qualitative Response Models Linear Probability Model Logit Model Probit Model in Python
16 Model Specification Omitted Variable Bias RESET Test AIC / BIC Selection
17 Panel Data Analysis Pooled OLS Fixed Effects Random Effects & Hausman Test
18 Autocorrelation Nature & Causes DW & BG Tests Cochrane-Orcutt in Python
19 Time Series: Basics Stationarity Unit Roots & ADF Test ACF / PACF in Python
20 Time Series: Forecasting ARIMA Models Forecast Evaluation Course Review
TipInteractive Slides

Click any week number to open that week’s slide index. All decks include live Python cells — run Python directly in your browser.