Introduction to Data Modeling, Statistics, and System Simulation

This course uses industrial engineering and operations research models for analyzing and optimizing real systems where the underlying processes and/or parameters are not fully known, but data may be available, sampled, or artifically generated. Monte Carlo simulations are used to model systems that may be too complex to approximate accurately with deterministic, stationary, or static models, and to measure the robustness of predictions, and manage the risks, in decisions based on data-driven industrial engineering and operations research models.