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 and Data Preparation 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.

*This course has been revised for fall 2021 and was formerly titled Advanced Modeling Techniques.


Fall 2022
Start/End DatesDay(s)TimeBuildingSection
09/20/22 - 12/10/22Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Bhatti, Chad
Online
Open

Spring 2023
Start/End DatesDay(s)TimeBuildingSection
03/27/23 - 06/10/23Optional Sync W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Bhatti, Chad
Online
Open
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