Master of Information Systems Management (Sever)
Total units required: 30
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 |
| CYBER 5563 | IT Governance & Risk Management | 3 |
| INFO 5517 | Operational Excellence and Service Delivery | 3 |
| INFO 5540 | IT Architecture & Infrastructure | 3 |
| INFO 5575 | Enterprise Data Management | 3 |
| INFO 5985 | Capstone( Should be completed in the final semester) | 3 |
| Required: 18 units | ||
| Electives: Choose 12 units | ||
Cybersecurity Emphasis | ||
| 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 |
| INFO 5560 | Cybersecurity Technical Fundamentals | 3 |
Management Emphasis | ||
| CYBER 5570 | Leadership Seminar for Technology Professionals | 3 |
| ETEM 5504 | Engineering Management and Financial Intelligence | 3 |
| ETEM 5505 | Decision Analysis and Optimization | 3 |
| ETEM 5527 | Entreprenuership:Challenges and Opportunities | 3 |
| ETEM 5582 | Human Performance in the Organization | 3 |
| ETEM 5587 | Communication Excellence for Influential Leadership | 3 |
| ETEM 5600 | Supply Chain Management | 3 |
Applied Data Analytics & Machine Learning Emphasis | ||
| INFO 5521 | Special Topics in Information Technology | 3 |
| INFO 5558 | Application of Deep Neural Networks | 3 |
| INFO 5559 | Applications of Generative AI and LLM | 3 |
| INFO 5574 | Foundations of Analytics | 3 |
| INFO 5576 | Analytics Applications(completion of INFO 5574 recommended) | 3 |
Mathematical Data Analytics Emphasis | ||
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4107 | Introduction to Machine Learning and Pattern Classification | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5107 | Machine Learning | 3 |
| ESE 4150 | Optimization | 3 |
| MATH 5020 | Mathematical Statistics | 3 |
AI & Machine Learning Emphasis | ||
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4107 | Introduction to Machine Learning and Pattern Classification | 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.