Pathway and Course-Focused Tracks
The Pathway track requires completion of 34.5 credit hours coursework credit and 6 credit hours of approved coursework. The Course-Focused MS degree program requirements consist of the completion of 30 credit hours of approved coursework. Both Pathway and Course-Focused tracks require satisfactory completion of a comprehensive exam, i.e., passing the course ENGR 600 with a grade of “P”. ENGR 600 is satisfactorily completed by achieving a grade of B or higher in each one of three courses in the student's depth area.
Project and Thesis Tracks
The Project-Focused track requires 24 credit hours of coursework credit and 6 credit hours of project (CSDS 695).
ENGR 600 consists of Comprehensive Exam questions that are administered in CSDS 410, CSDS 425, CSDS 440, CSDS 444, and CSDS 493. Students must take and pass questions in at least two of these classes. Students who fail one exam in a course may retake that exam one more time but are not required to retake the associated course. The Project-Focused track requires 24 credit hours of coursework credit and 6 credit hours of project (CSDS 695). The Thesis-Focused track requires 18 credit hours of coursework credit and 12 credit hours of thesis (CSDS 651). A Combined Bachelor's/Master's (CBM) student is required to choose the thesis-focused track initially.
Both the Thesis-Focused and the Project-Focused track require a formal written report, as well as a final oral examination by a committee of at least three faculty members, two of whom must be primarily affiliated with the CS program. The academic advisor is normally one of the committee members. For Project-Focused track students, the oral examination fulfills the Comprehensive Examination requirement of the School of Graduate Studies.
Track Transfer
If a student wishes to switch from one track to another, the following requirements apply:
- Deadline. In each semester, students must request to switch track one week before the date at which Drop/Add ends, as stated in the academic calendar.
- Pathway, Course-only, Course-only or Project to Thesis. A course-only student may request to switch to the thesis track only if they (1) have already taken at least 9 credit hours of letter graded CSDS courses and (2) have a GPA of 3.5 or higher and (3) have a TOEFL score of 90 or higher and (4) have the recommendation of a CDS advisor or (co)advisor.
- Pathway or Course-only to Project. A course-only student may request to switch to the project thesis track only if they (1) have a TOEFL score of 90 or higher and (2) have the recommendation of a CDS advisor or (co)advisor.
- Thesis to Project, or Thesis or Project to Course-only. Such a transfer needs approval from the student's advisor and the department chair.
- Petition. If a student fails to satisfy the transfer requirements, a petition may be submitted by a CDS advisor or (co)advisor to the department chair. In no case, petitions may be submitted by non-CDS faculty members or by students.
Students should consult with their academic advisor and/or department to determine the detailed requirements within this framework.
Course Requirements
For all tracks, at least 18 credit hours of coursework must be at the 400-level or above.
Students in the pathway track are required to take and pass CSDS 410 in their first semester, and to pass:
- By the end of the second semester, CSDS 401
- By the end of the first academic year or before 18 credit hours of coursework
- CSDS 410
- Any one course that is either listed as an undergraduate Computer Science Breadth Requirement or subsumes a computer science breadth course
Students failing the conditions above will be separated from further study in Computer Science. CSDS 401 cannot be counted toward the Course-focused, Project, or Thesis tracks.
All students are required to have specialized knowledge in at least one of the following depth areas, by taking at least three graduate-level classes from that area. The list of acceptable classes is shown below. For research or project-focused tracks, the chosen area should correspond to the student’s thesis research area or project in general. CSDS 600 classes will also qualify in this category with approval from the student’s advisor. The remaining classes can be (i) any other class from the classes listed below, or (ii) any letter graded CSDS class (see note below), or (iii) at most two graduate-level classes other than those in category (i) and (ii) (such as non-letter-graded graduate CSDS classes or graduate classes in other departments).
(Note: The Graduate School and the School of Engineering limit the number of undergraduate courses that can be taken for credit by Master students.)
Students should discuss their courses with their advisor every semester prior to registration. Students must achieve a grade point average of 3.0 or higher; it is computed for all of the letter-graded courses on the student's academic program.
List of Depth Areas and Corresponding Courses
| Code | Title | Credit Hours |
|---|---|---|
| 1. Algorithms & Theory | ||
| CSDS 410 | Analysis of Algorithms | 3 |
| CSDS 440 | Machine Learning | 3 |
| CSDS 455 | Applied Graph Theory | 3 |
| CSDS 456 | Data Privacy | 3 |
| CSDS 477 | Advanced Algorithms | 3 |
| MATH 408 | Introduction to Cryptology | 3 |
| 2. Artificial Intelligence | ||
| CSDS 440 | Machine Learning | 3 |
| CSDS 442 | Causal Learning from Data | 3 |
| CSDS 455 | Applied Graph Theory | 3 |
| CSDS 465 | Computer Vision | 3 |
| CSDS 491 | Probabilistic Models in AI | 3 |
| CSDS 496 | Sequential Decision Making | 3 |
| CSDS 497 | Foundations of Statistical Natural Language Processing | 3 |
| CSDS 499 | Algorithmic Robotics | 3 |
| ECSE 484 | Computational Intelligence I: Basic Principles | 3 |
| 3. Bioinformatics | ||
| CSDS 410 | Analysis of Algorithms | 3 |
| CSDS 435 | Data Mining | 3 |
| CSDS 440 | Machine Learning | 3 |
| CSDS 456 | Data Privacy | 3 |
| CSDS 458 | Introduction to Bioinformatics | 3 |
| CSDS 459 | Bioinformatics for Systems Biology | 3 |
| SYBB 412 | Survey of Bioinformatics: Programming for Bioinformatics | 3 |
| 4. Computer Networks & Systems | ||
| CSDS 425 | Computer Networks I | 3 |
| CSDS 427 | Internet Security and Privacy | 3 |
| CSDS 428 | Computer Communications Networks II | 3 |
| CSDS 438 | High Performance Data and Computing | 3 |
| CSDS 444 | Computer Security | 3 |
| ECSE 414 | Wireless Communications | 3 |
| 5. Databases & Data Mining | ||
| CSDS 433 | Database Systems | 3 |
| CSDS 435 | Data Mining | 3 |
| CSDS 440 | Machine Learning | 3 |
| STAT 426 | Multivariate Analysis and Data Mining | 3 |
| PQHS 471 | Machine Learning & Data Mining | 3 |
| 6. Security & Privacy | ||
| CSDS 427 | Internet Security and Privacy | 3 |
| CSDS 444 | Computer Security | 3 |
| CSDS 448 | Smartphone Security | 3 |
| CSDS 456 | Data Privacy | 3 |
| CSDS 493 | Software Engineering | 3 |
| MATH 408 | Introduction to Cryptology | 3 |
| 7. Software Engineering | ||
| CSDS 425 | Computer Networks I | 3 |
| CSDS 433 | Database Systems | 3 |
| CSDS 438 | High Performance Data and Computing | 3 |
| CSDS 442 | Causal Learning from Data | 3 |
| CSDS 444 | Computer Security | 3 |
| CSDS 448 | Smartphone Security | 3 |
| CSDS 493 | Software Engineering | 3 |