Introduction to Computer Simulation and Modeling

In a world of complex systems, testing changes in the real world is often too expensive, risky, or slow. Computer simulation allows you to build virtual environments to experiment safely and predict outcomes with confidence. This text-only course guides you from absolute beginner to confidently designing and running your own simulations. You will learn how to translate real-world scenarios—like queueing systems, financial risks, and physical processes—into clean, functional Python simulation code. What you'll learn: Understand core simulation concepts, including discrete event simulation and continuous modeling; Build Monte Carlo simulations to analyze risk and uncertainty in decision-making; Implement queueing and network models using Python simulation libraries; Apply structured statistical methods to analyze simulation input and output data; Design basic agent-based models to simulate complex interactions; Structure your simulation code using modern Python practices, including type hints and clean documentation. You will start with foundational terminology and the mathematics of randomness before moving into step-by-step written tutorials that build functional simulation models from scratch. This course is designed for beginners, developers, and analysts looking to understand modeling without needing advanced prior engineering degrees. A basic familiarity with Python is helpful but not required. Begin reading today to start modeling and simulating complex systems with precision.