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
| A minimum cumulative GPA of 3.0 is required | ||
| Course Requirements * | ||
| No more than 9 credit hours may be at the 400 level. | ||
| CS 796 | Graduate Seminar | 1 |
| Area of Concentration | ||
| Complete one Area of Concentration in any area: | 9 | |
One Core course | ||
Two Elective courses | ||
| Complete 6 additional credit hours core courses from the other two areas that are not the area of concentration. | 6 | |
| Elective Courses | 9 | |
| Choose 9 additional credit hours from the courses listed in any CS area of concentration | ||
| Complete 1 of the following options: | 7-9 | |
| Thesis Option - 7 hours | ||
| Graduate Seminar (1 hour) | ||
| Research (6 hours) | ||
Thesis | ||
Final Oral or Written Examination | ||
| Problem Report Option - 9 hours | ||
Complete a minimum 6 additional hours of coursework, at least 3 hours of which must be from the completed area of concentration. | ||
| Research (3 hours) | ||
Formal written report or professional report/paper | ||
Final Oral or Written Examination | ||
| Coursework Option - 9 hours | ||
Complete a minimum of 9 additional hours of coursework, at least 6 hours of which must be from the completed area of concentration. | ||
| Total Hours | 32-34 | |
| * | 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. |
Areas of Concentration
Computer Systems
| Core Courses | ||
| Advanced Networking Concepts | ||
| Switching Circuit Theory 1 | ||
| Elective Courses | ||
| Computer Incident Response | ||
| Application of Neural Networks | ||
| Applied Fuzzy Logic | ||
| Computer Data Forensics | ||
| Intro Computer Security Management | ||
| Sensor Actuator Networks | ||
| Data and Computer Communications | ||
| Developing Portable Software | ||
| Computer Forensics and the Law | ||
| Advanced Computer Systems Architecture | ||
| Distributed and Pervasive Compt | ||
| Computer Network Forensics | ||
Software/Knowledge Engineering
| Core Courses | ||
| Advanced Real-Time Systems | ||
| Advanced Data Mining | ||
| Empirical Methods in Software Engineering and Computer Science | ||
| Pattern Recognition | ||
| Elective Courses | ||
| Special Topics ( Advanced Biometrics) | ||
| Developing Portable Software | ||
| Multimedia Systems | ||
| Advanced Artificial Intelligence Techniques | ||
| Computer System Security | ||
| Computational Photography | ||
| Machine Learning | ||
| Computer Vision | ||
| Distributed Systems and Algorithms | ||
| Special Topics (Search-based Software Engineering, Software Reliability) | ||
| Advanced Image Processing | ||
| Software Verification and Validation | ||
Theory of Computing
| Core Courses | ||
| Formal Specification of Language | ||
| Advanced Analysis of Algorithms | ||
| Computational Complexity | ||
| Elective Courses | ||
| Compiler Construction | ||
| Design of Algorithms | ||
| Automata Theory | ||
| Discrete Mathematics 2 | ||
| Special Topics (Network Optimization) | ||
| String Algorithms | ||
| Special Topics (Fixed Parameter Algorithms) | ||
| Pattern Recognition | ||
| Algorithmic Graph Theory | ||
| Information Dissemination | ||
| Special Topics (Approximation Algorithms) | ||
| Special Topics (Randomized Algorithms) | ||
Final Examination
M.S. students following the thesis or problem report option must prepare a written research proposal. The proposal must be approved by the student's AEC at least one semester prior to the final oral 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.
All master’s students must defend their thesis or problem report at an oral exam, attended by all members of the committee.
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.
Suggested Plan of Study
The plan below illustrates the Thesis Option. It is important for students to take courses in the order specified as much as possible; all prerequisites and concurrent requirements must be observed. A typical M.S.C.S degree program that completes degree requirements in one and half years is as follows. Those students who lack course prerequisites may require more than three semesters of full-time study to complete the degree. Students with research assistantships may also require more than three semesters to complete the degree.
| First Year | |||
|---|---|---|---|
| Fall | Hours | Spring | Hours |
| Area of Concentration 1 Core Course | 3 | Area of Concentration 1 Elective Course | 3 |
| Area of Concentration 1 Elective Course | 3 | Area of Concentration 2 Core Course | 3 |
| Elective Course | 3 | Elective Course | 3 |
| CS 796 | 1 | CS 796 | 1 |
| 10 | 10 | ||
| Second Year | |||
| Fall | Hours | ||
| Area of Concentration 3 Core Course | 3 | ||
| Elective Course | 3 | ||
| CS 697 | 6 | ||
| 12 | |||
| Total credit hours: 32 | |||
Major Learning Outcomes
Computer Science
It is our goal that in the first five years after graduation our students will:
- Achieve success and proficiency in the Computer Science profession.
- Be recognized as leaders.
- Contribute to the well-being of society.
Areas of Emphasis Offered
- Computational Data Science
- Cybersecurity
Area of Emphasis in Computational Data Science
| Data Science Core * | ||
| CS 560 | Big Data Engineering | 3 |
| Select one of the following: | 3 | |
| Advanced Data Mining | ||
| Machine Learning | ||
| Pattern Recognition | ||
| Data Science Electives ** * | 6 | |
| Cyber-Security: | ||
| Computer Network Forensics | ||
| Cybersecurity and Big Data Analytics | ||
| Theoretical Foundations: | ||
| Advanced Analysis of Algorithms | ||
| Theory of Database Systems | ||
| String Algorithms | ||
| Image and Video Analytics: | ||
| Application of Neural Networks | ||
| Special Topics (3-D Computer Vision) | ||
| Computer Vision | ||
| Computational Photography | ||
| Software Engineering: | ||
| Empirical Methods in Software Engineering and Computer Science | ||
| Total Hours | 12 | |
| * | Students pursuing this area of emphasis must successfully complete a total of 12 hours in identified data science courses. To fulfill the requirements for the Area of Emphasis in Computational Data Science graduate students must successfully complete the following set of courses: two courses from the Data Science core, one of which must be CS 560 – Big Data Engineering, plus one other course from the Data Science core; and two courses (6 credit hours) from the Data Science electives listed in the accompanying table. Students are encouraged but not required to choose Data Science elective courses from the same topic area. This is intended to foster a more concentrated focus in the student’s data science expertise. Students may also, if they choose, take one of the Data Science core courses, not already taken, as a data science elective. |
| ** | Students may choose to take one optional elective course from a department other than the Lane Department of Computer Science and Electrical Engineering. Courses outside of the Lane Department to satisfy the elective requirements of this area of emphasis must be approved by the Lane Department’s Computational Data Science coordinator. |
Area of Emphasis in Cybersecurity
| A 3.0 GPA is required in AOE coursework. | ||
| Required Courses | ||
| CPE 536 | Computer Data Forensics | 3 |
| CPE 568 | Computer Network Forensics | 3 |
| CS 539 | Computer Forensics and the Law | 3 |
| Select one of the following: | 3 | |
| Intro Computer Security Management | ||
| Cybersecurity and Big Data Analytics | ||
| Total Hours | 12 | |