Reproducible Patient-Cohort Analysis for a Pharma AI Vendor
Overview
What this challenge is about.
Build a reproducible cohort analysis on synthetic patient data, generating HbA1c and survival curves. Earn a verifiable certificate.
The scenario
The vendor (around 90 staff) bills around USD 300k per real-world-evidence study and a reproducibility failure on a single study can poison a USD 5M master-services-agreement relationship.
The Brief
What you'll do, and what you'll demonstrate.
Rebuild a diabetes-cohort outcome analysis as a fully reproducible, auditable artifact on a public synthetic dataset.
Earning criteria — what you'll demonstrate
- Implement a reproducible analysis pipeline from raw data to chart
- Apply standard epidemiological methods (cohort definition, Kaplan-Meier)
- Document analytical choices to a level an external auditor accepts
- Use synthetic data responsibly to mirror a real clinical workflow
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Reproducible Analysis
Apply reproducible analysis to solve real industry problems and demonstrate production-level capability.
- Cohort Analysis
Apply cohort analysis to solve real industry problems and demonstrate production-level capability.
- Survival Analysis
Apply survival analysis to solve real industry problems and demonstrate production-level capability.
- Data Wrangling
Apply data wrangling to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Documentation
Apply documentation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Reproducible cohort + survival analysis on patient-like data is exactly the portfolio piece a junior data scientist at a pharma-AI or healthtech vendor wants to show.
This challenge sharpens
- cohort-analysis
- survival-analysis
- reproducible-analysis
Applied AI Scientist
Auditable analyses with rigorous decision logs are how applied AI scientists earn trust with regulated-industry stakeholders.
This challenge sharpens
- reproducible-analysis
- documentation
- cohort-analysis
MLOps Engineer
Environment pinning, one-command runs, and audit trails are the same disciplines MLOps engineers apply to production model pipelines.
This challenge sharpens
- reproducible-analysis
- documentation
- python