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 | ||
| Course Requirements * | ||
| No more than 9 credit hours may be at the 400 level. | ||
| Plan of Study | ||
| CS 796 | Graduate Seminar | 1 |
| Focus Area | ||
| Complete one Focus Area in any area: | 9 | |
One Core course | ||
Two Elective courses | ||
| Complete 6 additional credit hours from core courses from the other two areas that are not the primary focus area. | 6 | |
| Elective Courses | 9 | |
Choose any course listed in any focus area or complete an AOE in Computational Data Science or Cybersecurity ** | ||
| Complete 1 of the following options: | 6 | |
| Thesis Option - 7 hours | ||
| Graduate Seminar (1 hour) | ||
| Research (6 hours) | ||
Final Oral or Written Examination | ||
Thesis | ||
| Problem Report Option - 6 hours | ||
Complete a minimum 3 additional hours of coursework, at least 3 hours of which must be from the completed focus area. | ||
| Research (3 hours) | ||
Final Oral or Written Examination | ||
Formal written report or professional report/paper | ||
| Coursework Option - 6 hours | ||
Complete a minimum of 6 additional hours of coursework, at least 6 hours of which must be from the completed focus area. | ||
| Total Hours | 31 | |
- *
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.
- **
Students choosing to complete an AOE in Computational Data Science or Cybersecurity will be required to take additional credit hours to complete both AOE and degree.
Focus Areas
Computer Systems
| Code | Title | Hours |
|---|---|---|
| Core Courses | ||
| Advanced Networking Concepts | ||
| Switching Circuit Theory 1 | ||
| Elective Courses | ||
| Computer Incident Response | ||
| Application of Neural Networks | ||
| Deep Learning | ||
| Sensor Actuator Networks | ||
| Advanced Real-Time Systems | ||
| Data and Computer Communications | ||
| Developing Portable Software | ||
| Computer Forensics and the Law | ||
| Advanced Computer Systems Architecture | ||
| Distributed and Pervasive Compt | ||
| Computer System Security | ||
| Machine Learning | ||
| Pattern Recognition | ||
| Computer Vision | ||
| Digital Signal Processing for Radio Astronomy | ||
| Advanced Image Processing | ||
Software/Knowledge Engineering
| Code | Title | Hours |
|---|---|---|
| Core Courses | ||
| Application of Neural Networks | ||
| Advanced Real-Time Systems | ||
| Empirical Methods in Software Engineering and Computer Science | ||
| Pattern Recognition | ||
| Elective Courses | ||
| Deep Learning | ||
| Developing Portable Software | ||
| Big Data Engineering | ||
| Cybersecurity and Big Data Analytics | ||
| Advanced Data Mining | ||
| Computational Photography | ||
| Machine Learning | ||
| Computer Vision | ||
| Advanced Image Processing | ||
| Software Verification and Validation | ||
| Software Engineering of Mobile Applications | ||
Theory of Computing
| Code | Title | Hours |
|---|---|---|
| Core Courses | ||
| Formal Specification of Language | ||
| Advanced Analysis of Algorithms | ||
| Computational Complexity | ||
| Elective Courses | ||
| Compiler Construction | ||
| Design of Algorithms | ||
| Automata Theory | ||
| Discrete Mathematics 2 | ||
| String Algorithms | ||
| Pattern Recognition | ||
| Advanced Topics | ||
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.
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
Area of Emphasis in Computational Data Science
| Code | Title | Hours |
|---|---|---|
| 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
| Code | Title | Hours |
|---|---|---|
| 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 | |
Major Learning Outcomes
Computer Science
It is our goal that in the first few years after graduation our students will:
1. Achieve success and proficiency in the computer science profession, with demonstrable expertise, depth and breadth of knowledge in their chosen field in computer science.
2. Be recognized as leaders, by demonstrating enhanced capability and initiative in planning, implementing and disseminating innovative research and development projects leading to new discoveries that will advance the frontiers of knowledge in computer science and related fields.
3. Contribute to the well-being of society, by appreciating and understanding the critical role of computer science and its related disciplines, as well as professional and ethical considerations, in the well-being of individuals, communities, nations, and the global society at large.