Total Units Required: 30
Students must complete five out of the six required courses as well as the mandatory Capstone. In addition, they need to choose four electives. In order to earn the degree, all courses must be passed with a C– or higher. In addition, a student must have a cumulative grade point average of at least 2.70 over all courses applied toward the degree.
| Code | Title | Units |
|---|---|---|
| CYBER 5559 | Introduction to Cybersecurity | 3 |
| INFO 5517 | Operational Excellence & Service Delivery | 3 |
| INFO 5521 | Principles of Software Design & Architecture | 3 |
| INFO 5540 | IT Architecture & Infrastructure | 3 |
| INFO 5563 | IT Strategy, Governance, and Risk Management | 3 |
| INFO 5575 | Enterprise Data Management | 3 |
| INFO 5985 | MISM Capstone (mandatory; should be completed in the final semester) | 3 |
| Required: 18 units | ||
| Electives: Choose 12 units | ||
Cybersecurity Emphasis | ||
| CYBER 5560 | Cybersecurity Technologies and Threats | 3 |
| CYBER 5561 | Oversight for Excellence: Cybersecurity Management and Governance | 3 |
| CYBER 5562 | Efficient and Effective Cybersecurity Operations | 3 |
| CYBER 5563 | Enterprise Network Security | 3 |
| CYBER 5566 | Cybersecurity Risk Management | 3 |
| CYBER 5567 | The Hacker Mindset: Cyber Attack Fundamentals | 3 |
Management Emphasis | ||
| ETEM 5504 | Engineering Management & Financial Intelligence | 3 |
| ETEM 5505 | Decision Analysis & Optimization | 3 |
| ETEM 5582 | Human Performance in the Organization | 3 |
| ETEM 5587 | Communication Excellence for Influential Leadership | 3 |
| ETEM 5600 | Supply Chain for Engineering Managers | 3 |
Applied Data Analytics & Machine Learning Emphasis | ||
| INFO 5552 | Block Chain | 3 |
| INFO 5558 | Applications of Deep Neural Networks | 3 |
| INFO 5559 | Applications of Generative AI and Large Language Models | 3 |
| INFO 5574 | Foundations of Analytics | 3 |
| INFO 5576 | Analytics Applications (prior completion of INFO 5574 recommended) | 3 |
Mathematical Data Analytics Emphasis | ||
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4107 | Introduction to Machine Learning | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5107 | Machine Learning | 3 |
| ESE 4150 | Optimization | 3 |
| ESE 5200 | Probability and Stochastic Processes | 3 |
AI & Machine Learning Emphasis | ||
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4107 | Introduction to Machine Learning | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5107 | Machine Learning | 3 |
| CSE 5109 | Advanced Machine Learning | 3 |
Bridge Course* | ||
| INFO 5509 | Fundamentals of Information Technology | 3 |
- *
The bridge course is offered for students with limited to no information systems background. The successfully completed course will count toward the 12 required elective units.