Bayesian Health Data Science

Description from DukeHub

This course will teach students to analyze biomedical data using Bayesian inference, focusing on real-world data like electronic health records, wearables, and imaging. It covers hierarchical models for complex data, including missing, spatial, and longitudinal data. Students will also learn Bayesian machine learning techniques, such as regularization for high-dimensional data and scalable inference methods like variational inference. Additional topics may include causal inference, meta-analysis, and time-to-event data. The course combines mathematical theory with practical skills in R and Stan, preparing students to address complex biomedical research problems using Bayesian methods.

Skills

• data and statisticsdescription: "bayesian" and 3 more
• programmingAI review: "practical skills in R and Stan"
• ai and machine learningAI review: "Bayesian machine learning techniques"

AI-resilient: 0 of 5 0/5

• No: Physical, lab or field work
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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