CS 590M: Introduction to Simulation

Prof. Peter J. Haas

Spring, 2020

Course Prerequisites

This course, being interdisciplinary, requires sufficient background in both Python programming and in probability and statistics. Official prerequisites are CS 187 and STAT 515, or permission from the instructor. More details are given below.

Programming:

Students need to be able to write, run, and debug basic programs in Python. Computing competency at the level of CS 187 should suffice; Python is a relatively easy language to learn, and there are lots of available online resources. (Python is one of the most popular languages for Data Science, and so is good to know.) The computer assignments will be relatively straightforward from a CS point of view: no fancy GUIs or data structures. The programs, although due every one or two weeks, are relatively short, typically 3-4 screenfuls of code. Sample Python code will be posted at the beginning of the course, and students may adapt and build on this code, if they want, to complete the first assignment. Each subsequent assignment will build upon the previous assignment. Students may work in teams of up to 2 people on the programming assignments. Moreover, for a few of the homework questions, students will be allowed to use R and Excel if they find these environments easier to work with. During the first week or so of class, there will be a review session covering the basics of writing, running, and debugging a Python programming.

Probability and Statistics:

Students need to be proficient in basic calculus-level probability and statistics at the level of STAT 515. For probability, this means, e.g., being able to work with probability density functions and knowing what the central limit theorem is. For statistics, this means basic familiarity with point estimates and confidence intervals. Knowledge of basic stochastic processes, such as Markov chains, is helpful, but not required; the necessary material will be covered in class. This handout briefly reviews the probability and statistics basics needed for the course. (There is a small amount of simulation-oriented material that many students may not know, but should be relatively self-explanatory.) If you have a great deal of difficulty understanding the handout, then you may not have sufficient background preparation for the course. There will be a probability and statistics review session at the beginning of the course, and the handout points to some basic probability and statistics textbooks.

Last modified 14 January 2020