ORIE 4580
Course description (from class roster):Introduction to Monte Carlo simulation and discrete-event simulation. Topics include: random variate and process generation; data-driven distribution modeling; input and output analysis; modeling, analysis and optimization of complex systems. Emphasizes tools and techniques needed in practice; in particular, modeling in simulation in Python, as well as commercial discrete-event simulation languages.
Offered: Fall.
Prerequisites: CS 2110/ENGRD 2110.
Corequisites: ORIE 3500, with permission of instructor.
Is Python used?
Yes, heavily.
If Python is used, where is it used?
Python is used in homeworks, labs, and even on exams. The NumPy library figures heavily in course content, as it is used in generating many different random distributions.
Python is also used for a large project at the end of the semester (although a few students may choose to use Simio or something else instead).
What is Python used for?
The labs use Python in Jupyter Notebooks, but you can use whatever you want on the homeworks. As mentioned above, NumPy is used a lot in class and also in homeworks and labs, and on the final project. You may have to write Python code on an exam, or at least reference various NumPy functions.
The final project involves a complex event simulation, which NumPy and MatPlotLib are really helpful with.
What do I need to know?
You should have a decent-to-thorough understanding of Python; at the minimum, you should be familiar with the material covered in CS 1110, and maybe have some algorithm knowledge on the level of CS 2110. (Actually, CS 2110 is a prerequisite for this course.)