Course Descriptions and Schedule
Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.
MSDS 454-DL : Applied Probability and Simulation Modeling
Description
This advanced modeling course begins by reviewing probability theory and models. Students learn principals of random number generation and Monte Carlo methods for classical and Bayesian statistics. They are introduced to applied probability models and stochastic processes, including Markov Chains, exploring applications in business and scientific research. Students work with open-source and proprietary systems, implementing discrete event and agent-based simulations. This is a case study and project-based course with an extensive programming component.
Recommended prior course: MSDS-DL 460 Decision Analytics.
Prerequisites: (1) MSDS 420-DL Database Systems or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.
Prior to fall 2021, this course was titled "Advanced Modeling Techniques."
Spring 2025 | ||||
Start/End Dates | Day(s) | Time | Building | Section |
03/31/25 - 06/13/25 | Sync Session W | 7 – 9:30 p.m. | 55 | |
Instructor | Course Location | Status | CAESAR Course ID | |
Bhatti, Chad | Online | Open |