Lecture Library
RevealJS Slide Decks — Data InDeed
All lecture slide decks for the Data InDeed curriculum — built in RevealJS with live R visualizations, speaker notes, and chalkboard mode. Each deck links back to the corresponding blog series for deeper reading.
How to use: Open any slide deck in your browser. Press S for speaker view, B for chalkboard, F for fullscreen.
Series
Applied Statistics for AI & Clinical Decision-Making
10 lectures · Posts 01–30 · ~8.5 hours
The foundational series — probability, inference, regression, Bayesian methods, model evaluation, and the mathematical foundations of modern AI. Grounded throughout in trauma systems and clinical decision-making.
Browse lectures → Master Speaker Notes
Advanced Statistics for AI & Clinical Decision-Making
4 lectures · Posts 01–10 · ~3.5 hours
Missing data mechanisms and imputation, causal inference frameworks, identification strategies (IV, RD, DiD), and evidence synthesis. Prerequisite: Applied Statistics series.
Browse lectures → Master Speaker Notes
Design of Experiments for AI & Clinical Decision-Making
4 lectures · Posts 01–10 · ~3.5 hours
Study design from first principles — RCT anatomy, longitudinal design and power, trial integrity and blinding, quasi-experimental methods and synthesis. For analysts who design or appraise clinical studies.
Browse lectures → Master Speaker Notes
Trauma Registry Analytics
5 lectures · Posts 01–15 · ~4.5 hours
Registry data fundamentals, Bayesian modeling philosophy, missing data in hierarchical models, prediction of rare outcomes, and production governance. Focused on DoDTR and military trauma system contexts.
Browse lectures → Master Speaker Notes
OMOP & Interoperability
2 lectures · Posts 01–05 · ~2 hours
OMOP CDM architecture and its limits for trauma data, interoperability governance, value-level metadata, trauma-ready CDM extensions, and the translation layer model.
Browse lectures → Master Speaker Notes
Ethics of Clinical AI
4 lectures · Posts 01–12 · ~3.5 hours
Opacity and accountability failures, prediction vs. responsibility, fairness and algorithmic monitoring, semantic infrastructure and responsible AI governance — with a focus on military trauma and the DoDTR modernization agenda.
Browse lectures → Master Speaker Notes
Quick Reference
| Series | Lectures | Hours | Status |
|---|---|---|---|
| Applied Statistics | 10 | ~8.5 | ✅ Complete |
| Advanced Statistics | 4 | ~3.5 | ✅ Complete |
| Design of Experiments | 4 | ~3.5 | ✅ Complete |
| Trauma Registry Analytics | 5 | ~4.5 | ✅ Complete |
| OMOP & Interoperability | 2 | ~2.0 | ✅ Complete |
| Ethics of Clinical AI | 4 | ~3.5 | ✅ Complete |
| Total | 29 | ~25.5 |