There are two options in the Master’s program: the thesis option and the non-thesis option. The non-thesis option requires coursework only, while the thesis option involves completing a thesis under the guidance of a research advisor, with fewer required courses. Both options are equally considered for admission, with no preference or priority given to either.

For the thesis option, students are responsible for identifying and securing a research advisor. This typically involves reviewing faculty research areas, contacting potential advisors, and obtaining their agreement to supervise your thesis work.

Thesis Option Requirements

• CS 5200 – Algorithms
• 1 semester of seminar course (CS 6010)
• A minimum of 9 credit hours of 6000-level CS lecture courses
• 6–9 credit hours of thesis (CS 6099)
• A total of 31 credit hours of graduate coursework and thesis credit is required

Master’s students may change their degree option at any time during their studies.

General Information

The Computer Science department offers comprehensive Master’s degree programs focusing on computer network security, software engineering, web databases, wireless systems, intelligent systems, data mining, parallel and distributed processing, pervasive computing, computer networks, scientific visualization, and algorithms. These research activities support the department’s two major areas of excellence: software engineering and critical infrastructure protection.

Admission Requirements

• Minimum undergraduate GPA: 3.0/4.0
• GRE: Verbal + Quantitative 300, Analytical Writing 3.0
• English proficiency: TOEFL 80, IELTS 6.5, PTE 58, Duolingo 115
• Official transcripts required
• Applicants are expected to have strong mathematical skills and competency in a modern programming language

Program Requirements

The Master’s program requires the completion of a total of 31 credit hours of graduate coursework:

• CS 5200 – Algorithms
• 1 semester of seminar course (CS 6010)
• A minimum of 9 credit hours of 6000-level CS lecture courses
• 9–15 credit hours of graduate CS lecture courses at or above the 5000 level
• Total of 31 credit hours of graduate coursework required
• Out-of-department course requirements:
  • Prior to Spring 2025 intake: 3–6 credit hours from the approved list
  • Spring 2025 intake or later: Maximum of 3 credit hours from the approved list

Core Computer Science Courses (4 credit hours)

• CompSci 5200: Analysis of Algorithms (3 credit hours, offered every semester)
• CompSci 6010: Seminar (1 credit hour, offered every semester)

5000-Level Elective Computer Science Courses (15–18 credit hours)

• CompSci 5204: Regression Analysis (Spring)
• CompSci 5205: Real-Time Systems (Spring)
• CompSci 5300: Database Systems (Fall)
• CompSci 5400: Introduction to Artificial Intelligence (Spring)
• CompSci 5401: Evolutionary Computing (Fall)
• CompSci 5402: Introduction to Data Mining (Spring)
• CompSci 5403: Introduction to Robotics (Fall)
• CompSci 5404: Introduction to Computer Vision (Fall)
• CompSci 5405: Java GUI & Visualization (Spring)
• CompSci 5406: Interactive Computer Graphics (Spring)
• CompSci 5407: Introduction to Virtual Reality (Fall)
• CompSci 5408: Game Theory for Computing (Fall)
• CompSci 5409: Applied Social Network Analysis (Fall)
• CompSci 5420: Introduction to Machine Learning (Fall)
• CompSci 5480: Introduction to Deep Learning (Spring)
• CompSci 5600: Advanced Computer Networks (Spring)
• CompSci 5601: Security Operations & Program Management (Fall)
• CompSci 5602: Introduction to Cryptography (Fall)
• CompSci 5700: Bioinformatics (Spring)
• CompSci 5800: Distributed Computing (Fall)
• CompSci 5802: Introduction to Parallel Programming & Algorithms (Fall)
• CompSci 5803: High Performance Computer Architecture (Spring)

6000-Level Elective Computer Science Courses (Minimum 9 credit hours)

• CompSci 6110: Advanced Computer Architecture (Fall)
• CompSci 6202: Markov Decision Processes (Fall)
• CompSci 6204: Applied Graph Theory (Spring)
• CompSci 6304: Cloud Computing and Big Data Management (Fall)
• CompSci 6400: Advanced Topics in Artificial Intelligence (Fall)
• CompSci 6401: Advanced Evolutionary Computing (Fall)
• CompSci 6405: Clustering Algorithms (Spring)
• CompSci 6406: Machine Learning in Computer Vision (Fall)
• CompSci 6407: Internet of Things with Data Science (Fall)
• CompSci 6604: Mobile, IoT and Sensor Computing (Fall)
• CompSci 6800: Distributed Systems Theory and Analysis (Spring)
• CompSci 6801: Topics in Parallel and Distributed Computing (Spring)

A full list of course availability and timing can be found here: Course Availability

More information:
Graduate Catalog

Please note: During the semester in which a student completes nine credit hours of graduate coursework, the student must formally plan the remainder of their program in consultation with their academic advisor and submit a Form 1 for approval.