Curriculum in Masters of Science in Computer Science
A candidate for the M.S. degree in computer science must comply with the rules and regulations as outlined in the WVU Graduate Catalog and the specific requirements of the Statler College and the Lane Department of Computer Science and Electrical Engineering.
Program Requirements
All M.S. degree candidates are required to perform research and follow a planned program of study. The student’s research advisor, in conjunction with the student’s Advising and Examining Committee (AEC) will be responsible for determining the plan of study appropriate to the student’s needs. The underlying principle of the planned program is to provide the students with the necessary support to complete their degree and prepare them for their career.
Curriculum Requirements
| Code | Title | Hours |
|---|---|---|
| A minimum cumulative GPA of 3.0 is required | ||
| At least 9 credit hours of coursework must have a CS subject code * | ||
| No more than 9 credit hours may be at the 400 level | ||
| Course Requirements ** | ||
| Plan of Study | ||
| CS 796 | Graduate Seminar | 1 |
| Core Course Requirements + | ||
| CPE 553 | Advanced Networking Concepts | 3 |
| CS 520 | Advanced Analysis of Algorithms | 3 |
| Select one of the following: | 3 | |
| Application of Neural Networks | ||
| Empirical Methods in Software Engineering and Computer Science | ||
| Pattern Recognition | ||
| Elective Courses | 6 | |
| Select two of the following: | ||
| Deep Learning | ||
| Big Data Engineering | ||
| Cybersecurity and Big Data Analytics | ||
| Advanced Data Mining | ||
| String Algorithms | ||
| Machine Learning | ||
| Computer Vision | ||
| Stochastic Systems Theory | ||
| Advanced Image Processing | ||
| Area of Emphasis or Additional Electives | 9 | |
| Select one of the following options: | ||
Area of Emphasis ++ | ||
Departmental and STEM Electives (see table below for courses) | ||
| Complete 1 of the following options: | 6-7 | |
| Thesis Option - 7 hours | ||
| Graduate Seminar (1 hour) | ||
| Research (6 hours) | ||
Final Oral or Written Examination | ||
Thesis | ||
| Problem Report Option - 6 hours | ||
Select an additional 3 credit hours of coursework from CPE, CS, EE, CYBE, MATH, PHYS, or STAT courses 400-795, as approved by the AEC | ||
| Research (3 hours) | ||
Final Oral or Written Examination | ||
Formal written report or professional report/paper | ||
| Coursework Option - 6 hours | ||
Select an additional 6 credit hours of coursework from CPE, CS, EE, CYBE, MATH, PHYS, or STAT courses 400-795, as approved by the AEC | ||
| Total Hours | 31-32 | |
| Code | Title | Hours |
|---|---|---|
| Additional Electives List | ||
| Departmental Electives (3 hours) | ||
Any EE, CPE, CS, or CYBE courses 400-795, as approved by the student's AEC | ||
| STEM Electives (6 hours) | ||
Any EE, CpE, CS, CYBE, ASTR, BMEG, CE, CHE, CHEM, IENG, MAE, MATH, MINE, PHYS, PNGE, or STAT courses 400-795, as approved by the student's AEC | ||
- *
Excluding CS 796 and CS 697/797
- **
Students who do not hold a baccalaureate degree in computer science are required to take a set of undergraduate computer science courses above and beyond the minimum coursework requirements.
- +
Courses in this block not used to complete the Core Courses requirements can be used as Electives or Additional Departmental Electives
- ++
Courses completed for the Area of Emphasis cannot fulfill other degree requirements.
Final Examination
All students following the thesis or problem report option are required to pass a final oral or written examination, administered by their AEC, covering the thesis or problem report and/or related course material.
A student who fails the research defense may repeat the defense at most once, at a time determined by the AEC but not necessarily during the same semester.
Areas of Emphasis Offered
- Artificial Intelligence and Computational Data Science
- Cyber-Physical and Complex Systems
- Cybersecurity and Networked Systems
Artificial Intelligence and Computational Data Science Area of Emphasis
| Code | Title | Hours |
|---|---|---|
| Select one of the following: | 3 | |
| Application of Neural Networks | ||
| Deep Learning | ||
| Select two of the following: | 6 | |
| Advanced Data Mining | ||
| Pattern Recognition | ||
| Machine Learning | ||
| Total Hours | 9 | |
Cyber-Physical and Complex Systems Area of Emphasis
| Code | Title | Hours |
|---|---|---|
| Select three of the following: | 9 | |
| Hardware Security and Trust | ||
| Stochastic Systems Theory | ||
| Power Distribution Systems | ||
EE 540 | Data Analytics for Secure Cyber-Power Systems | |
| Communication Theory | ||
| Total Hours | 9 | |
Cybersecurity and Networked Systems Area of Emphasis
| Code | Title | Hours |
|---|---|---|
| Select three of the following: | 9 | |
| Hardware Security and Trust | ||
| Intro Computer Security Management | ||
| Advanced Networking Concepts | ||
| Advanced Cybersecurity Principles | ||
| Ethics in Cybersecurity | ||
| Total Hours | 9 | |
Major Learning Outcomes
Computer Science
Upon graduation, Computer Science MS students will be able to:
1. Identify, design and implement solutions to real-world challenges using expertise across key areas of computer science
2. Effectively disseminate innovative research and projects through written and oral formats as demonstrated through presentations, papers, and other publications
3. Develop skills in teamwork, life-long learning, and professionalism as related to the field of computer science.