Appl Stats Anlys Env Natur Sci

Description from DukeHub

Graphical and exploratory data analysis; modeling, estimation, and hypothesis testing; analysis of variance; random effect models; regression and scatterplot smoothing; generalized linear models; resampling and randomization methods. Concepts and tools involved in data analysis. Special emphasis on examples drawn from the social and environmental sciences. Students to be involved in applied work through statistical computing using software, STATA or R.

Skills

• data and statisticsdescription: "data analysis" and 4 more
• lab workhas a lab component
• programmingAI review: "statistical computing using software, STATA or R"

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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