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.