Bayesian Statistical Modeling and Data Analysis

Description from DukeHub

Principles of data analysis and modern statistical modeling. Exploratory data analysis. Introduction to Bayesian inference, prior and posterior distributions, hierarchical models, model checking and selection, missing data, introduction to stochastic simulation by Markov chain Monte Carlo using a higher level statistical language such as R or Matlab. Applications drawn from various disciplines. Not open to undergraduate students or students who have taken Statistical Science 360. Recommended prerequisite: Statistical Science 611 or the following: Statistical Science 210 and (Statistical Science 230 or 240L) and (Mathematics 202, 202D, 212, or 222) and (Mathematics 216, 218, or 221, any of which may be taken concurrently).

Skills

• data and statisticsdescription: "data analysis" and 10 more
• lab workhas a lab component

AI-resilient: 1 of 5 1/5

• ✓Yes: Physical, lab or field work · "laboratory"
• 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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