# Syllabus: AINS6101 Predictive Analytics in Population Health

## Catalog Description

Uses AI and statistical models for risk stratification, forecasting, interventions, equity, and public-health governance.

## Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

## Weekly Schedule

| Week | Topic | Essential Question | Deliverable |
|------|-------|--------------------|-------------|
| 1 | Population health data ecosystems | What data sources describe health at population scale? | Lab notebook + assignment brief |
| 2 | Risk stratification and prediction | How are patients grouped for intervention? | Lab notebook + assignment brief |
| 3 | Causal inference and confounding | Why is prediction not the same as cause? | Lab notebook + assignment brief |
| 4 | Forecasting and surveillance | How do models support trend detection? | Lab notebook + assignment brief |
| 5 | Equity and social determinants | How do models account for structural health drivers? | Lab notebook + assignment brief |
| 6 | Intervention targeting and evaluation | How should interventions be prioritized and measured? | Lab notebook + assignment brief |
| 7 | Privacy and public-health governance | What safeguards are required for sensitive population data? | Lab notebook + assignment brief |
| 8 | Population analytics portfolio | What evidence supports a public-health recommendation? | Lab notebook + assignment brief |

## Assessment

| Component | Weight |
|-----------|--------|
| Weekly labs and notebooks | 30% |
| Applied assignments | 35% |
| Participation and technical critique | 15% |
| Final synthesis portfolio | 20% |

## Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.