Data Analytics for Systems Engineering

4 credits

Emphasizes data-driven system modeling, including basic statistical learning models, and system modeling and decision-making. Covers experimental design for data collection, tree-based control charts for process monitoring, rule-based decision-making, and diagnosis of root causes as learning problems. Students develop connections between emerging statistical learning techniques with system modeling and optimization methods. Prerequisite: either IND E 315, STAT 390, or STAT 391; recommended: basic programming skills; and experience with R programming language or Matlab.