IENG 390 Computational Mathematics
This course provides a practical introduction to computational mathematics with a strong emphasis on the development of programming skills for mathematical problem solving. The course introduces essential tools for numerical modeling, data handling, and simulation. Selected numerical analysis topics such as root-finding, Gaussian elimination, interpolation, and error analysis are included. The course also delves into stochastic models, including Markov chains, Poisson processes, and queueing theory, and implementation of Monte Carlo simulations to analyze real-world systems. Throughout the course, students gain experience writing code and interpreting analytical and computational results in business and industry contexts.
Prerequisites
4 Undergraduate credits
Effective May 6, 2026 to present
Learning outcomes
General
- Develop numerical algorithms to solve diverse computational problems.
- Design Monte Carlo simulation experiments to model systems subject to random events.
- Apply Markov processes to model stochastic systems.
- Analyze queuing processes using steady-state analysis.
- Interpret computational and analytical findings in business and industry contexts.