Community Detection on a Pharma Clinical-Trial Investigator Graph
Overview
What this challenge is about.
Build an investigator graph from oncology trials, run Louvain versus Leiden community detection, and label top communities. Earn a verifiable certificate.
The scenario
The pharma-AI startup (around 50 people, working with mid-cap pharma clients) typically spends 8 weeks of analyst time mapping investigator networks per program; cutting that to 2 weeks unlocks faster trial site selection and saves around USD 80,000 per program.
The Brief
What you'll do, and what you'll demonstrate.
Map the global oncology investigator landscape with graph-based community detection to accelerate trial-site selection.
Earning criteria — what you'll demonstrate
- Construct meaningful graphs from public registry data
- Apply Louvain and Leiden community-detection algorithms
- Characterize communities qualitatively and quantitatively
- Visualize large graphs for non-technical stakeholders
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.
- Community Detection
Apply community detection to solve real industry problems and demonstrate production-level capability.
- Louvain
Apply louvain to solve real industry problems and demonstrate production-level capability.
- Leiden
Apply leiden to solve real industry problems and demonstrate production-level capability.
- Graph Analysis
Apply graph analysis to solve real industry problems and demonstrate production-level capability.
- Network Visualization
Apply network visualization to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Mapping a real-world domain via graph community-detection and shipping a stakeholder-ready atlas is exactly the day-one work of a data scientist at any pharma-AI firm.
This challenge sharpens
- community-detection
- graph-analysis
- network-visualization
Data Engineer
Building reproducible graph-construction pipelines from messy public data is core data-engineering work in knowledge-intensive companies.
This challenge sharpens
- graph-analysis
- python
- community-detection
Applied AI Scientist
Combining algorithmic choices (Louvain vs. Leiden) with operational delivery (atlas + memo) is the applied-AI-scientist craft for analytics-heavy teams.
This challenge sharpens
- louvain
- leiden
- community-detection