Data and Decision Analytics with Python

3 credits · College of Engineering catalogue

Advisory Prerequisite: Introductory probability and statistics, linear algebra and coding in Python. Enforced Prerequisite: Graduate Standing. (3 credits) A foundational introduction to statistical modeling and decision analytics for engineering and operations with an emphasis on Python-based implementations. Topics include probability, inference, hypothesis testing, and experimental design, as well as decision analytics through decision trees, optimization models, and simulation methods such as Monte Carlo and discrete-event simulation. Emphasizes modeling thinking, with hands-on Python practice in data visualization, cleaning, and implementation to support modern machine learning and data-driven decision-making.