Design of Experiment and Analytics

Catalog description

This course covers classical and modern Design of Experiments (DOE) techniques for engineering and industrial applications. Students will learn factorial and fractional factorial designs, response surface methodology, and Taguchi methods, along with advanced approaches such as machine learning-assisted DOE, Bayesian optimization, adaptive experimental designs, and digital twin-based experimentation. Emphasis is placed on AI-driven data analytics, simulation-based DOE, and real-world case studies. Prerequisite: ENT 333 , or STA 261 or STA 301 , or equivalent.