This course is designed to teach you linear programming from the ground up, using Python as a practical tool to model and solve optimization problems. Whether you are a student, an engineer, or someone interested in decision science, you will find clear explanations and hands-on coding examples that connect theory to application.
We begin with the fundamentals: what linear programming is, how objective functions and constraints work, and why these models are so widely used in industries such as logistics, manufacturing, and operations management. Each concept is explained in plain language, and mathematical expressions are read out naturally, for example, ‘three x plus two y is less than or equal to one hundred.’
After understanding the basics, you will move into Python implementation. We use libraries that make it easy to define and solve optimization problems. You will learn how to translate a real situation into a mathematical model, write it in Python, and interpret the solution. Along the way, we will address common mistakes and clarify points that usually confuse beginners.
By the end of this course, you will have the ability to set up your own optimization models, test them with data, and use Python to find the best solutions. The skills you gain here are practical, transferable, and highly useful for anyone interested in optimization and applied problem solving.
- This course is for students, engineers, analysts, and anyone curious about optimization and decision science. It is also suitable for Python learners who want to see how programming can be applied to solve real business and engineering problems.
