This is the home of the course CHME5137 at Northeastern University, taught by Richard West (@rwest).

This course should equip chemical engineering students to create a computational model of any physical, chemical, or biological system, and perform numerical experiments on the model to learn the significance of parameters and model assumptions. The course will integrate thermodynamics, kinetics, transport, and mathematics, with applications in chemistry, biology, and materials science. Faced with a modeling challenge, students will learn to define the problem, split it into sub-systems, develop mathematical models of each sub-system, implement these in Python, and thus construct a model to represent the whole process. Monte Carlo, uncertainty analysis, and global sensitivity analysis, and Bayesian parameter estimation methods will then be used to test and learn from the model. Students will also learn essential software carpentry skills, such as using the Linux command prompt, version control, and distributed computing on a cluster. Recent students also report learning a lot "strategies for debugging, which was very helpful for coding but also more broadly as researchers", "critical thinking skills that are needed in programming to both produce quality code and debug issues that pop up"

Maybe you want to read the Syllabus.

I thoroughly enjoyed my time in CHME5137. The course material was challenging and engaging, and I learnt a lot about python and programming in general. Now that I've graduated, ... I'm excited to apply what I learned in CHME5137. I only wish there were more courses like this in the ChemE curriculum.

I had high expectations for the course, and was still pleasantly surprised by the course scope. I have a much better grasp on many concepts that I knew I wanted to better understand. There were just as many topics (version control in programming, LaTeX, convergence analysis) that I didn't anticipate needing until you presented them, but will surely save me from headaches later. I feel more equipped than ever before to tackle some of the engineering problems in my near future.

I learned a ton in this class, and now I feel much more confident about my ability to use modeling and python to strengthen my research. Thank you for making a safe and fun space to learn, and for your encouragement along the way.

The course was very enjoyable and covered multiple concepts I look forward to implementing into my research.

Thank you for the feedback and for a great course! I feel like I know Python and how to approach new challenges related to it much better now than I did at the start of the course. The introduction to shell scripting was also fascinating and helpful.
Thank you again for a great course! I may be looking to implement Bayesian parameter estimation in the coming weeks at work to calibrate several flowmeters in a flow process that depend on the viscosity and speed of sound of the fluid.