Foundations & descriptive analysis
Lessons 01–02L · 01
A Structured Approach to Data AnalysisCausal diagrams, data-collection sheets, coding and entry, file and variable management, and program-mode versus interactive workflows.
Open module
L · 02
Data Cleaning & Descriptive AnalysesData quality assessment, cleaning strategies, handling missing data, descriptive statistics, and visualization for epidemiologic datasets.
Open module
Regression models
Lessons 03–04L · 03
Linear & Logistic RegressionLinear regression for continuous outcomes and logistic regression for binary outcomes (coefficients, odds ratios, hypothesis tests, diagnostics, ROC analysis), then the model-building strategies that decide which predictors enter a model and in what form.
Open module
L · 04
Generalized Linear ModelsMultinomial, proportional-odds, adjacent-category, and continuation-ratio models for multi-category outcomes, then Poisson and negative binomial regression, offsets, overdispersion, and zero-adjusted models for counts and rates.
Open module
Time-to-event & dependent data
Lessons 05–06L · 05
Survival DataKaplan-Meier estimation, Cox proportional hazards, parametric survival models, and frailty models.
Open module
L · 06
Modelling Dependent DataClustered and hierarchical data, ICC and design effects, linear mixed models for continuous outcomes, GLMMs for discrete outcomes, and repeated-measures analysis with correlation structures and GEE.
Open module