The Duke MS in Interdisciplinary Data Science (IDS) immerses students in machine-learning, causal inference, and human-centered design. Cohort teams tackle sponsored projects—predicting coral bleaching, auditing algorithmic bias, optimizing refugee-aid logistics—under paired faculty and industry mentors. Ethics modules anchor every sprint, ensuring models serve public interest as well as accuracy metrics.
Federated-learning pipeline for rural diabetic-retinopathy screening
Causal graph analysis of housing policy on homelessness flows
Real-time wildfire spread model integrating satellite thermal data
Differential-privacy dashboard for municipal open-data portals
Reinforcement-learning drones optimizing mangrove reforestation
Bias audit toolkit for large-language models in hiring platforms
Climate-risk scorecard for smallholder farms using remote sensing
NLP sentiment tracker of public trust in vaccination campaigns
Explainable deep-learning model for energy-grid fault prediction
Blockchain-based land-title registry reducing legal disputes
VR data-storytelling exhibit on prison-population trends
AutoML pipeline selecting green-chemistry reaction routes
Shiny app visualizing historical redlining and modern air quality
Graph neural network identifying poaching hotspots from ranger logs
Open-source library for ethics-integrated model-documentation cards
Citizen-science drone image classifier improving flood response
Policy brief generator summarizing analytics for non-technical officials
Serious game teaching students about algorithmic fairness trade-offs
Automatic provenance tracking for reproducible research workflows
Harness data for human and planetary well-being with Duke IDS.
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