Computer Science
Major: Computer Science
Degree Awarded: Master of Science in Computer Science (MSCS) or Doctor of Philosophy (PhD)
Calendar Type: Quarter
Total Credit Hours: 45.0 (MSCS); 48.0 (PhD)
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 11.0701
Standard Occupational Classification (SOC) code: 11-3021; 15-1111; 15-1131; 15-1132; 15-1199
About the Program
The Department of Computer Science in the College of Computing & Informatics houses research groups actively conducting research on a wide range of topics in Computer Science including artificial intelligence, algorithms, computer vision and graphics, programming languages, networks, privacy and security, high-performance computing, software engineering, computer algebra, and algorithms. The department emphasizes both interdisciplinary and applied research and is supported by major federal research grants from the National Science Foundation, Department of Defense, Department of Energy, and the National Institute of Standards and Technology, as well as by private sources.
Master of Science in Computer Science
The Master of Science in Computer Science program is designed to provide breadth of understanding in the core topics of computer science, in-depth advanced material, and a range of topics in the research areas of the faculty. A balance of theory and practice is presented, preparing students to perform cutting edge research as well as training students to become practicing computer scientists or software engineers in business, industry, or government. A thesis option is available to prepare students for doctoral studies or other research-oriented career paths.
The program provides room for electives outside of Computer Science in an area which the student wishes to apply their computing skills and from the Post-Baccalaureate Certificate in Computer Science (for those with an insufficient Computer Science background).
A graduate co-op is available for the Master of Science in Computer Science program. For more information, visit the Steinbright Career Development Center's website.
Doctorate in Computer Science
Students enrolled in the PhD in Computer Science program are expected to become an expert in a research area in computer science or its interdisciplinary field with other disciplines. They are expected to conduct research in considerable depth, and make substantial contributions through creative research and serious scholarship. The program is designed for students to ensure core knowledge of the fundamental computer science areas and to conduct bleeding edge research at the forefront of a selected area. Students are prepared for leadership careers in research and education in computer science and interdisciplinary work using computer science.
Additional Information
For more information about these programs, including admission requirements, visit the College of Computing & Informatics website.
Master of Science in Computer Science
Students must complete a minimum of 45.0 graduate credits for the MS degree.
| Core Requirements | ||
| Students must take 1 course from each pair | ||
| CS 500 | Fundamentals of Databases | 3.0 |
| or CS 510 | Introduction to Artificial Intelligence | |
| CS 521 | Data Structures and Algorithms I | 3.0 |
| or CS 525 | Theory of Computation | |
| CS 530 | Developing User Interfaces | 3.0 |
| or CS 540 | High Performance Computing | |
| CS 536 | Computer Graphics | 3.0 |
| or CS 583 | Introduction to Computer Vision | |
| CS 543 | Operating Systems | 3.0 |
| or CS 544 | Computer Networks | |
| CS 550 | Programming Languages | 3.0 |
| or SE 575 | Software Design | |
| Major Electives | 15.0 | |
| Students must take an additional 5 CS-related courses from the following categories | ||
| From the courses listed below, excluding any courses taken to fulfill a core requirement and spanning at least 2 categories | ||
Theory | ||
| Data Structures and Algorithms I (Core Candidate) | ||
| Data Structures and Algorithms II | ||
| Theory of Computation (Core Candidate) | ||
| Algorithmic Game Theory | ||
| Advanced Data Structure and Algorithms | ||
| Approximation Algorithms | ||
| Computational Geometry | ||
Intelligent Systems | ||
| Fundamentals of Databases (Core Candidate) | ||
| Introduction to Artificial Intelligence (Core Candidate) | ||
| Robot Laboratory | ||
| Advanced Artificial Intelligence | ||
| Game Artificial Intelligence | ||
| Knowledge-based Agents | ||
| Machine Learning | ||
| Deep Learning | ||
| Data Analysis at Scale | ||
| Responsible Data Analysis | ||
Programming Systems | ||
| Programming Languages (Core Candidate) | ||
| Program Generation and Optimization | ||
| Reverse Software Engineering | ||
| Parallel Programming | ||
| Software Design (Core Candidate) | ||
| Software Reliability and Testing | ||
| Software Architecture | ||
| Security Engineering | ||
Computer Systems | ||
| Operating Systems (Core Candidate) | ||
| Computer Networks (Core Candidate) | ||
| Advanced Operating Systems | ||
| Network Security | ||
| Distributed Systems Software | ||
Vision and Graphics | ||
| Computer Graphics (Core Candidate) | ||
| Interactive Computer Graphics | ||
| Game Engine Programming | ||
| Introduction to Computer Vision (Core Candidate) | ||
| Advanced Computer Vision | ||
| Advanced Computer Graphics | ||
Applications | ||
| Developing User Interfaces (Core Candidate) | ||
| High Performance Computing (Core Candidate) | ||
| Applied Symbolic Computation | ||
| Privacy | ||
| Cognitive Systems | ||
| Computer Algebra I | ||
| Computer Algebra II | ||
| From MSSE Core Courses | ||
| Software Design | ||
| Software Reliability and Testing | ||
| Requirements Engineering and Management | ||
| Software Project Management | ||
| From the following MSDS Core Courses | ||
| Data Acquisition and Pre-Processing | ||
| Data Analysis and Interpretation | ||
| Additional Graduate-Level Courses | 12.0 | |
| 4 additional graduate level courses are required, which could be: | ||
Up to 6 credits for the thesis option | ||
Up to 2 CS Independent Studies | ||
Additional approriate graduate level Computer Science, Software Engineering, Data Science or Artificial Intelligence courses, consulting with your advisor courses | ||
Up to 2 appropriate graduate-level computing-related courses outside of Computer Science, Software Engineering, Data Science and Artificial Intelligence approved by the College | ||
From the core courses from the CS-PBC | ||
| Introduction to Programming | ||
| Data Structures and Algorithms | ||
| Systems Basics | ||
| Introduction to Software Design | ||
| Total Credits | 45.0 | |
Sample Plan of Study (MSCS)
| First Year | |||||||
|---|---|---|---|---|---|---|---|
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| Core Courses | 6.0 | Core Courses | 6.0 | Core Courses | 6.0 | Major Electives | 6.0 |
| 6 | 6 | 6 | 6 | ||||
| Second Year | |||||||
| Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
| Major Electives | 6.0 | Major Elective | 3.0 | Electives | 6.0 | Elective | 3.0 |
| Elective | 3.0 | ||||||
| 6 | 6 | 6 | 3 | ||||
| Total Credits 45 | |||||||
Note: Second Year Summer is less than the 4.5-credit minimum required (considered half-time status) of graduate programs to be considered financial aid eligible. As a result, aid will not be disbursed to students this term.
PhD in Computer Science
| Core Requirements | 18.0 | |
| Students must take 1 course marked “Core Candidate” from each of the 6 categories below. There are 2 Core Candidate courses in each category. | ||
Theory | ||
| Data Structures and Algorithms I (Core Candidate) | ||
| Theory of Computation (Core Candidate) | ||
| Data Structures and Algorithms II | ||
| Algorithmic Game Theory | ||
| Advanced Data Structure and Algorithms | ||
| Approximation Algorithms | ||
| Computational Geometry | ||
Intelligent Systems | ||
| Fundamentals of Databases (Core Candidate) | ||
| Introduction to Artificial Intelligence (Core Candidate) | ||
| Robot Laboratory | ||
| Advanced Artificial Intelligence | ||
| Game Artificial Intelligence | ||
| Knowledge-based Agents | ||
| Machine Learning | ||
| Deep Learning | ||
| Data Analysis at Scale | ||
| Responsible Data Analysis | ||
Programming Systems | ||
| Programming Languages (Core Candidate) | ||
| Program Generation and Optimization | ||
| Reverse Software Engineering | ||
| Parallel Programming | ||
| Software Design (Core Candidate) | ||
| Software Architecture | ||
| Software Reliability and Testing | ||
| Security Engineering | ||
Computer Systems | ||
| Operating Systems (Core Candidate) | ||
| Computer Networks (Core Candidate) | ||
| Advanced Operating Systems | ||
| Network Security | ||
| Distributed Systems Software | ||
Vision and Graphics | ||
| Computer Graphics (Core Candidate) | ||
| Introduction to Computer Vision (Core Candidate) | ||
| Interactive Computer Graphics | ||
| Game Engine Programming | ||
| Advanced Computer Vision | ||
| Advanced Computer Graphics | ||
Applications | ||
| Developing User Interfaces (Core Candidate) | ||
| High Performance Computing (Core Candidate) | ||
| Applied Symbolic Computation | ||
| Privacy | ||
| Cognitive Systems | ||
| Computer Algebra I | ||
| Computer Algebra II | ||
| Breadth Requirement | 12.0 | |
| Students must take another 4 intermediate and advanced courses from the remaining courses above, spanning at least 3 of the listed course categories while earning at least a grade of B in each course. | ||
| Depth Requirement | 15.0 | |
| Students are required to complete at least 15.0 credits of CS courses beyond the Breadth Requirement. These courses should be Computer Science (CS) and Software Engineering (SE) courses preferably at the 600- or 700-level courses. Course selection must be approved by the student’s research advisor. The department will periodically offer topics courses, typically run in a seminar fashion, on current research areas of interest to faculty. As part of the Depth Requirements, 3.0 out of the 15.0 credits, but no more than 9.0 credits, are to be Independent Study work (CS I799 or CS I999). | ||
| Research | 3.0-45.0 | |
| Independent Study in CS | ||
| Research in Computer Science | ||
| Ph.D. Dissertation | ||
| Total Credits | 48.0-90.0 | |
Plan of Study
Upon entering the PhD program, each student will be assigned an Graduate Advisor, and with the help of the Advisor will develop and file a plan of study (which can be brought up to date when necessary). The plan of study should be filed with the Graduate Advisor no later than the end of the first term.
Qualifying Requirements
PhD student must pass each of the six core courses selected as part of the "Core Requirements" (one "Core Candidate" course from each of the listed categories) with a grade B+ or higher. If a student fails to meet this minimum grade requirement, they may either (1) take the other "Core Candidate" course in the same category and obtain a grade of B+ of higher; (2) retake the same course at the next offering; or (3) retake the final exam of the same course with permission by the instructor, if deemed appropriate by the instructor and the College. Normally, a student is expected to satisfy this requirement by the end of the student's first year. These requirements, including the remedial actions, must be completed by the end of the student's second year. Transfer credits may count towards these requirements subject to course instructor approval of the syllabus for the transferred course.
Candidacy Exam
The Computer Science candidacy examination serves to define the student’s research domain and to evaluate the student’s knowledge and understanding of various fundamental and seminal results in that domain. At this point the student is expected to be able to read, understand, analyze, and explain advanced technical results in a specialized area of computer science at an adequate level of detail. The candidacy examination will evaluate those abilities using a defined set of published manuscripts. The student will prepare a written summary of the contents of the material, present the summary orally, and answer questions about the material. The examination committee will evaluate the written summary, the oral presentation, and the student’s answers.
Thesis Proposal
After completing the candidacy examination successfully, the PhD candidate must prepare a thesis proposal that outlines, in detail, the specific problems that will be solved in the PhD dissertation. The quality of the research proposal should be at the level of, for example, a peer-reviewed proposal to a federal funding agency, or a publishable scientific paper. The candidate is responsible for sending the research proposal to the PhD committee two weeks before the oral presentation. The PhD committee need not be the same as the candidacy exam committee, but it follows the same requirements and must be approved by the Office of Graduate Studies. The oral presentation involves a 30-minute presentation by the candidate followed by an unspecified period during which the committee will ask questions. After the question and answer period, the candidate will be asked to leave the room and the committee will determine if the research proposal has been accepted. The research proposal can be repeated at most once.
Thesis Defense
After completing the research proposal successfully, the PhD candidate must conduct the necessary research and publish the results in a PhD dissertation. The dissertation must be submitted to the PhD committee two weeks prior to the oral defense. The oral presentation involves a 45-minute presentation by the candidate, open to the public, followed by an unspecified period during which the committee will ask questions. The question-and-answer period is not open to the public. After the question and answer period, the candidate will be asked to leave the room and the committee will determine if the candidate has passed or failed the examination. The candidate will be granted one more chance to pass the final defense if they fail it the first time. Paperwork selecting the thesis committee and indicating the results of the thesis defense must be filed with the College of Computing & Informatics and the Graduate College.
Dual Degree Opportunities
Graduate students already enrolled in a master's degree program at Drexel have the opportunity, through the dual master's program, to work simultaneously on two CCI master's degrees and to receive both upon graduation. To be eligible, graduate students must be currently working on their first CCI master's degree when requesting admission to the second CCI master's degree. They must obtain approval from the graduate advisors of both programs and work out a plan of study encompassing coursework and/or research (thesis) credits for both degrees.
Some courses may be used to satisfy requirements in both degrees, reducing the total number of courses taken, according to Drexel’s Dual MS Degree Policy. The dual degree for MSCS students is only available to on-campus students. Please contact your advisor for more information on program requirements as some CCI master's degree combinations may require additional prerequisites.
The dual master's student must complete the Change of Curriculum and Status form and obtain approvals from both graduate advisors. Final approval is granted by the Graduate College. The student is then registered in both majors simultaneously. Upon graduation, the student must file two Application for Degree forms.
3675 Market Street
In March 2019, the College of Computing & Informatics relocated to 3675 Market. For the first time in the College's history, all CCI faculty, students and professional staff are housed under one roof. Occupying two floors in the brand new uCity Square building, CCI's new home offers state-of-the-art technology in our classrooms, labs, meeting areas and collaboration spaces. 3675 Market offers Class A laboratory, office, coworking, and convening spaces. In fall 2019, the College opened a third floor which will include additional offices, classrooms, innovative research labs, and a maker space. Located at the intersection of Market Street and 37th Street, 3675 Market will act as a physical nexus, bridging academic campuses and medical centers to the east and south, the commercial corridors along Market Street and Chestnut Street, and the residential communities to the north and west.
The uCity Square building offers:
- Speculative lab/office space
- World-class facilities operated by CIC
- Café/restaurant on-site
- Quorum, a two-story, 15K SF convening space and conference center
- Adjacent to future public square
- Access to Science Center’s nationally renowned business acceleration and technology commercialization programs
Drexel University Libraries
Drexel University Libraries is a learning enterprise, advancing the University’s academic mission through serving as educators, supporting education and research, collaborating with researchers, and fostering intentional learning outside of the classroom. Drexel University Libraries engages with Drexel communities through three physical locations, including W. W. Hagerty Library, Queen Lane Library, and the Library Learning Terrace, as well as a vibrant online presence which sees, on average, over 8,000 visits per day. In the W.W. Hagerty Library location, College of Computing & Informatics students have access to private study rooms and nearly half a million books, periodicals, DVDs, videos and University Archives. All fields of inquiry are covered, including: library and information science, computer science, software engineering, health informatics, information systems, and computing technology. Resources are available online at library.drexel.edu or in-person at W. W. Hagerty Library.
The Libraries also make available laptop and desktop PC and Mac computers, printers and scanners, spaces for quiet work or group projects and designated 24/7 spaces. Librarians and library staff—including a liaison librarian for computing and informatics—are available for individual research consultations and to answer questions about materials or services.
CCI Commons
Located on the 10th floor of 3675 Market Street, the CCI Commons is an open lab and collaborative work environment for students. It features desktop computers, a wireless/laptop area, free black and white printing, and more collaborative space for its students. Students have access to 3675 Market's fully equipped conference room with 42” displays and videoconferencing capabilities. The CCI Commons provides technical support to students, faculty, and professional staff. In addition, the staff provides audio-visual support for all presentation classrooms within 3675 Market. Use of the CCI Commons is reserved for all students taking CCI courses.
The computers for general use are Microsoft Windows and Macintosh OSX machines with appropriate applications which include the Microsoft Office suite, various database management systems, modeling tools, and statistical analysis software. Library related resources may be accessed at the CCI Commons and through the W.W. Hagerty Library. The College is a member of the Rational SEED Program which provides cutting-edge software development and project management software for usage in the CCI Commons and CCI classrooms. The College is also a member of the Microsoft Academic Alliance known also as “DreamSpark” that allows students free access to a wide array of Microsoft software titles and operating systems.
The CCI Commons, student labs, and classrooms have access to networked databases, print and file resources within the College, and the Internet via the University’s network. Email accounts, Internet and BannerWeb access are available through the Office of Information Resources and Technology.
CCI Learning Center
The CCI Learning Center (CLC), located in 3675 Market Street's CCI Commons student computer lab, provides consulting and other learning resources for students taking computer science classes. The CLC is staffed by graduate and undergraduate computer science students from the College of Computing & Informatics.
The CLC and CCI Commons serve as a central hub for small group work, student meetings, and TA assistance.
Research Laboratories
The College houses multiple research labs, led by CCI faculty, in 3675 Market Street including: the Drexel Health and Risk Communication Lab, Interactive Systems for Healthcare, Socio-Technical Studies Group, Intelligent Information & Knowledge Computing Research Lab, Evidence-based Decision Making Lab, Applied Symbolic Computation Laboratory (ASYM), High Performance Computing Laboratory (SPIRAL), Drexel Research on Play (RePlay) Laboratory, Software Engineering Research Group (SERG), Social Computing Research Group, Vision and Cognition Laboratory (VisCog) and the Vision and Graphics Laboratory. For more information on these laboratories, please visit the College’s research web page.
