Schedule

FOUR COURSES · SIXTEEN WEEKS · EIGHTY LABS

Every lab, deck, and script in one table

A linked index of every lab, every slide deck, and every R script.

The schedule below is a dependency graph, not a rigid calendar. Dates and pacing are deliberately left blank: work through it at the speed your week allows, skip labs whose topic you already know, and revisit the ones that sting. Each row links to three artefacts — the HTML article for reading, the Reveal.js deck for presenting, and the plain R script for experimenting.

Course 1 — Foundations of Biostatistics with R

Week	#	Topic	Article	Slides	Script
1	1	Scientific process & research workflow	lab	slides	R
1	2	R, RStudio, Quarto, renv toolchain	lab	slides	R
1	3	Data types, tidy data, measurement quality	lab	slides	R
1	4	Import, joins, missingness with dplyr	lab	slides	R
1	5	ggplot2 grammar and multi-panel layouts	lab	slides	R
2	6	Descriptives and Table 1 with gtsummary	lab	slides	R
2	7	Probability and Bayes’ theorem	lab	slides	R
2	8	Diagnostic testing: sensitivity, specificity, LR	lab	slides	R
2	9	Discrete distributions: Bernoulli, binomial, Poisson	lab	slides	R
2	10	Continuous distributions and Q-Q plots	lab	slides	R
3	11	Populations, samples, CLT by simulation	lab	slides	R
3	12	Bootstrap and permutation	lab	slides	R
3	13	Maximum likelihood and standard errors	lab	slides	R
3	14	One-sample t and one-proportion	lab	slides	R
3	15	Hypothesis testing philosophy	lab	slides	R
4	16	Two-sample and paired t; effect sizes	lab	slides	R
4	17	Two proportions and goodness-of-fit	lab	slides	R
4	18	Pearson and Spearman correlation	lab	slides	R
4	19	Non-parametric tests	lab	slides	R
4	20	Sample size, power, Quarto reporting	lab	slides	R

Course 2 — Regression, ANOVA & Model Diagnostics

Week	#	Topic	Article	Slides	Script
1	1	Correlation vs regression; Model-I/II	lab	slides	R
1	2	Simple linear regression	lab	slides	R
1	3	Multiple regression: confounding & interaction	lab	slides	R
1	4	Diagnostics: residuals, leverage, Cook, VIF	lab	slides	R
1	5	Robust and weighted regression; HC SEs	lab	slides	R
2	6	One-way ANOVA and contrasts	lab	slides	R
2	7	Two-way / factorial ANOVA	lab	slides	R
2	8	RCBD and repeated measures	lab	slides	R
2	9	GAMs with mgcv	lab	slides	R
2	10	Non-linear regression with nls	lab	slides	R
3	11	Logistic regression	lab	slides	R
3	12	ANCOVA in RCTs	lab	slides	R
3	13	Ordinal and multinomial regression	lab	slides	R
3	14	Poisson and negative-binomial regression	lab	slides	R
3	15	Calibration, discrimination, ROC/AUC, Brier	lab	slides	R
4	16	Dichotomisation, change scores, RTM	lab	slides	R
4	17	Kappa, ICC, Bland-Altman	lab	slides	R
4	18	Survival primer: KM, log-rank, Cox	lab	slides	R
4	19	Decision curves, NRI/IDI	lab	slides	R
4	20	Explanation vs prediction; reporting guidelines	lab	slides	R

Course 3 — Study Design, Longitudinal Data & Causal Inference

Week	#	Topic	Article	Slides	Script
1	1	Observational designs and STROBE	lab	slides	R
1	2	Randomised controlled trials	lab	slides	R
1	3	Adaptive, non-inferiority, equivalence trials	lab	slides	R
1	4	Bench and translational design	lab	slides	R
1	5	Power: closed-form and simulation-based	lab	slides	R
2	6	MCAR, MAR, MNAR	lab	slides	R
2	7	Multiple imputation with mice	lab	slides	R
2	8	Linear mixed models with lme4	lab	slides	R
2	9	GLMMs and GEE	lab	slides	R
2	10	Time series basics	lab	slides	R
3	11	Time-varying covariates, landmark, immortal-time	lab	slides	R
3	12	Competing risks and multistate models	lab	slides	R
3	13	DAGs with dagitty	lab	slides	R
3	14	Propensity scores and IPTW	lab	slides	R
3	15	G-methods, IV, DiD, RDD; HTE	lab	slides	R
4	16	Systematic reviews and PRISMA	lab	slides	R
4	17	Meta-analysis basics	lab	slides	R
4	18	Network meta-analysis	lab	slides	R
4	19	SIR / SEIR with deSolve	lab	slides	R
4	20	Pre-registration and SAPs	lab	slides	R

Course 4 — Modern Statistical Learning & High-Dimensional Biomedicine

Week	#	Topic	Article	Slides	Script
1	1	Cross-validation, nested CV, bootstrap	lab	slides	R
1	2	Regularisation: ridge, lasso, elastic net	lab	slides	R
1	3	PCA, FA, CCA, LDA	lab	slides	R
1	4	Clustering: k-means, hierarchical, model-based	lab	slides	R
1	5	UMAP and t-SNE	lab	slides	R
2	6	Trees, random forests, boosting	lab	slides	R
2	7	Interpretability and SHAP	lab	slides	R
2	8	Tabular neural networks with torch	lab	slides	R
2	9	Imaging and sequence models intro	lab	slides	R
2	10	tidymodels pipelines	lab	slides	R
3	11	Bayesian thinking; α vs decision error	lab	slides	R
3	12	brms / Stan, LOO, hierarchical	lab	slides	R
3	13	Biomarker statistics	lab	slides	R
3	14	Survival ML	lab	slides	R
3	15	Time-dependent Brier, IPA, external validation	lab	slides	R
4	16	RNA-seq with DESeq2/edgeR	lab	slides	R
4	17	Enrichment analysis	lab	slides	R
4	18	scRNA-seq with Seurat	lab	slides	R
4	19	FDR, knockoffs, replication crisis	lab	slides	R
4	20	TRIPOD-AI, fairness, reproducibility at scale	lab	slides	R