Advanced Methods of Applied Data Analysis

Description from DukeHub

Established, fast, and robust statistical tools for processing raw data into analyzable form, reducing its dimension, extracting signal, and producing calibrated predictions and uncertainty quantification under constraints of speed and scale. Topics include: numerical foundations; transformations and normal-scores/copula methods; distances and similarity graphs; nearest-neighbor methods; kernel density estimation and spline smoothing; dimension reduction and manifold methods; EM/MM and matrix factorization; alignment and Procrustes methods; signal extraction and filtering; advanced resampling; classification and calibration via isotonic regression; simulation design.

Skills

• data and statisticstitle: "data analysis"

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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