Module 0
Loading CSV Data in Python
Load wage_sample.csv, inspect rows, and identify variables in a DataFrame.
pandasCSV loadingDataFrame preview
Live notebooks
Run Python in the lesson
These JupyterLite-style notebooks are real `.ipynb` files students can download. They also run in the browser with Python, pandas, NumPy, and matplotlib so learners can execute cells and see results without installing anything.
Module 0
Descriptive Statistics in Python
Calculate means, medians, standard deviations, and grouped wage summaries.
summary statisticsgroupbyinterpretation
Module 0
Creating Simple Graphs in Python
Create a scatter plot that compares education and wages.
matplotlibscatter plotsvisual interpretation
Module 0
Mini Python Practice Lab
Run a full beginner workflow: load, inspect, summarize, graph, and interpret.
workflowsummariesgraphs
Module 1
First Python Data Example
Use Python to describe the wage and education relationship without overclaiming causality.
correlationgrouped meanscareful interpretation
Module 2
Simple Regression in Python
Estimate wage on education, draw the fitted line, and read the coefficient output with a statsmodels-compatible fallback.
OLSstatsmodels-readyfitted line
Module 2
Fitted Values and Residuals
Calculate fitted wages, residuals, squared residuals, and residual plots from the wage regression.
fitted valuesresidualsdiagnostic plots
Module 2
OLS and R-squared by Formula
Compute the OLS slope, intercept, residual sum of squares, and R-squared step by step.
OLS formulasSSRR-squared
Module 2
Module 2 Regression Project Template
A reusable project notebook students can edit to choose y and x, estimate the model, and write an interpretation.
project workflowstudent templateinterpretation