Optimization & Operations Research

About this course

This mid-level course is for learners heading toward a career as an operations research analyst. It builds on Calculus & Linear Algebra and Probability & Statistical Inference and turns them into the tools OR analysts use to recommend decisions. The first module covers linear programming: formulating decision variables, objectives and constraints, the graphical method and the simplex method, and solving models in Python with scipy's linprog and PuLP. The second module adds integer and network models: binary variables for yes/no decisions, shortest paths and the transportation problem, and staff scheduling. The third module deals with uncertainty: M/M/1 and multi-server queueing formulas, discrete-event simulation of a service line, and decision trees with expected value and the value of information. The last module is about practice: reading shadow prices and reduced costs, scoping a supply-chain project from the first meeting to the handover, and presenting a model to managers, with a look at OR careers. Eight lessons end with a Python exercise you run in your browser (numpy is available; scipy and PuLP are taught in the reading), and the examples use small, made-up data. Many OR roles ask for a master's degree in operations research, industrial engineering or analytics; the course helps you prepare and build a portfolio but does not replace that education.