Course Schedule, Part-time Online Program

Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.

Online Sync Sessions are an integral part of the online learning experience. Additional information about learning concepts and assignments may be discussed and sync sessions offer valuable opportunities for students to interact with their faculty and peers during the term. We encourage all students to attend live, but if they are unable to, sync sessions will be recorded and posted within Canvas to allow for an asynchronous model of success as well.

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 - 12/10/22Sync Session 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 - 06/10/23Sync Session W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Bhatti, Chad
Online
Open
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