Python Statistical Workflow
Set up the workflow for reproducible statistical analysis.
Statistical Data Analytics with Python
A reproducible Python statistical report with preparation, uncertainty, tests, effects, regression, diagnostics, and limitations.
Preparing Data For Statistical Analysis
Make sure analysis data matches the statistical question.
Descriptive Statistics And Statistical Visualisation
Summarise evidence before testing.
Simulation, Bootstrap, And Confidence Intervals
Use Python to make uncertainty visible.
Hypothesis Testing And Test Selection
Teach a repeatable test-selection workflow.
Comparing Groups And Categorical Data
Apply common comparison methods carefully.
Effect Sizes And Practical Significance
Teach magnitude, not only significance.
Correlation And Simple Linear Regression
Teach relationship analysis without causal overclaiming.
Multiple Regression And Diagnostics
Add predictors while keeping interpretation honest.
Logistic Regression And Binary Outcomes
Teach binary outcome modelling separately from linear regression.
Experiments, A/B Tests, And Causal Caution
Apply statistical analysis to experiments without overclaiming.
Capstone Statistical Report
Bring the full workflow together.
First-release boundary
This is a text-first course shell. Leonardo/image creation, external review, learner pilot evidence, and production publication remain separate later tasks.