Contents
- Applicants
- Multiple Degree Programs
- Graduation
- Semester Course Offerings
- Concentrations
- Cybersecurity (CYS)
- Data Mining and Intelligent Systems (DMIS)
- Software Engineering (SE)
- Documenting Transferred Courses
Applicants
Please see https://onlinemscs.utk.edu for program and recruiting information.
Applicants: Please see our topics list before you apply. Please see our website to speak with an enrollment advisor.
This page is to assist students who have been accepted into the program and are looking for a quick reference of which courses they should take. Courses will be offered to allow students to take both a depth of courses and a breadth across concentrations. Students should take their core and focused courses as soon as they are offered.
Multiple Degree Programs
If you are taking additional classes for a separate concentration or certificate, those courses do not count towards this degree. Only the courses listed below in your selected concentration will count towards the degree.
Graduation
Please see https://gradschool.utk.edu/academics/graduation/graduation-deadlines/ to know when to file for graduation.
See https://gradschool.utk.edu/academics/graduation/steps-to-graduation/ for the steps to graduation. The online MSCS program is a course-only, non-thesis degree program, which you can find under the “Master’s/EdS Non-Thesis Programs and Graduate Certificates” heading.
When you have all of your semester grades, excluding the semester prior and the semester you plan on graduating, please email me (Stephen Marz, your advisor: sgm AT utk.edu) the following:
- Your admission to candidacy form, properly filled out.
- Write your name and split out Last, then First, then Middle (leave blank if you don’t have a middle name).
- Double check your UT student ID and write in the format XXX-YY-ZZZZ.
- Use your UT email address: netid@vols.utk.edu.
- The major is “Computer Science”
- The degree program is “Masters of Science”
- Click the Distance Education (online only) radio button.
- The form must be e-signed and NOT an e-facsimile of your signature. There is an example in the link below.
- Please see https://gradschool.utk.edu/forms-central/applying-electronic-signatures/ for help applying an e-signature to a PDF document.
- The form must list all of your courses you plan to apply to your degree in chronological order (oldest first, most recent at the bottom). Therefore, you must have at least ten (10) courses listed, including transferred courses.
- Year/Term
- Use two letters for the semester FA (fall), SP (spring), SU (summer)
- Use four digits for the year (e.g., 2024)
- Separate by a front slash.
- Example: FA/2024, SP/2027, SU/2028
- Use COSC or ECE for the Course Name Prefix
- The Course # is the three digit code, such as 522 (for Machine Learning).
- The Course Title is the name of the course, such as Machine Learning, Algorithms, etc.
- The Hours will always be 3.
- The Grade is the final letter grade recorded on your transcript.
- Leave the grade box for any course in progress or in the future blank.
- Re-write your name and student ID (e.g., XXX-YYY-ZZZ) on the top of the fourth page.
- Year/Term
- If you have any courses you want to transfer from another institution, please see below under Transfer Courses.
- Do NOT use “print-to-PDF”. The form must contain the PDF fill in boxes at the bottom.
- I recommend using Adobe Acrobat Reader to fill out the PDF.
- Email your PDF file as an attachment to me (sgm@utk.edu).
- Do not use Adobe’s “share” feature. It must be sent to me as an attachment
I will forward the signed form for the second signature, and if everything is correct, it will be forwarded and submitted to the graduate school. Unless there is something wrong with your form, the next contact you will get regarding your form is your approval/denial from the graduate school.
Transfer Credits
If you have already taken some classes towards an MS in Computer Science from an accredited university or institute, up to four courses or twelve credit hours (whichever is lower) may be transferred to use towards your MSCS degree. However, these courses must be approved by your advisor (typically me) and will be documented on your MS candidacy form.
Only courses that are close to the courses you need to take for your concentration can be transferred. For example, an industrial engineering class cannot be transferred since it doesn’t closely resemble any of the courses you need for your master’s degree. The close enough will be judged by your advisor. This is why a syllabus and typical topics are important when making the decision on whether to accept a transfer course, or not.
Restrictions
The following restrictions apply (from the graduate catalog). The course you wish to transfer must…
- Be taken for graduate credit.
- Be a course transcribed for graduate credit and in which the student earned at least a grade of B.
- Not have been used for a previous degree.
- Be approved by the student’s graduate committee and the Dean of the Graduate School on the Admission to Candidacy form.
- The equivalent course has not been attempted at UT.
NOTE: Courses transferred to any graduate program will not affect the minimum residence requirements for the program, nor will they be included in calculating the student’s UT grade point average.
Documenting Transferred Courses
NOTE: If you plan on transferring courses from another school, please include the following in your email to me. You will also add the classes you want to transfer on your admission to candidacy form at the bottom after you list all of your UT courses. There’s a section for transfer courses.
- The syllabus of each and every course you plan on transferring.
- The online MSCS degree course you plan on substituting for your transferred course.
- A copy of the transcript for which you took the course.
- The transcript must clearly show the grade you received for the course.
- The transcript’s course number and year must clearly match the syllabus.
- State plainly and clearly your certification that the course(s) you plan on transferring was not used for any other conferred degree.
- WARNING: All we can do as a department is determine if an external course covers the same topics as one of ours. If you attest to not using the course towards another degree, you will be responsible for: (1) the truth of that statement and (2) proving that statement if the graduate school request clarification.
Course Offerings
Register for classes at https://onestop.utk.edu/class-registration/add/.
Registrar calendars can be found at the registrar’s office: https://registrar.utk.edu.
Course Schedules
Course schedules can be found here: https://tiny.utk.edu/mscs_schedule
Course Information
Course information for some offerings can be found here: https://tiny.utk.edu/mscs_courses (Login w/ UT ID)
Registration Requirements
Students are required to register for at least one (1) course in the fall or spring semesters. Students are not required to register for courses in the summer.
Should a student not be registered for any courses in the fall or spring semesters, he or she will be considered withdrawn from the program and will require a readmission.
See https://gradschool.utk.edu/admissions/applying-to-graduate-school/readmission/ for information about readmission.
Concentrations
Each concentration requires you take two (2) core courses, at least four (4) focused courses, and several electives for a total of 10 courses. Students may count any additional focused course as an elective.
We recommend that new students take no more than two courses in their admitted semester. Then, as the student becomes accustomed to the format and courses, they could take additional courses.
Students must earn a cumulative 3.0 GPA out of 4.0 to remain in the program. When a student dips below 3.0, he or she will be placed on academic probation. Students who are on probation for too long may be removed from the program.
Students who earn a D or F in a graduate class will not have that class count towards the degree program. The student may apply to retake the course. Students may only retake up to two graduate courses in the program.
Cybersecurity
Core (must take both)
COSC530: Computer Systems Organization
COSC566: Software Security
Focused (must take a minimum of 4)
COSC533: Cloud and Web Architectures
COSC534: Network Security
COSC559: Human-Computer Interaction
COSC561: Compilers and Runtime Systems
COSC569: Human Factors in Cybersecurity
COSC583: Applied Cryptography
ECE553: Computer Networks
Electives
COSC522: Machine Learning
COSC523: Artificial Intelligence
COSC524: Natural Language Processing
COSC525: Deep Learning
COSC526: Data Mining
COSC540: Advanced Software Engineering
COSC545: Digital Archeology
COSC565: Databases and Scripting Languages
COSC581: Algorithms
ECE517: Reinforcement Learning
Data Mining and Intelligent Systems
Core (must take both)
COSC522: Machine Learning
COSC523: Artificial Intelligence
Focused (must take a minimum of 4)
COSC524: Natural Language Processing
COSC525: Deep Learning
COSC526: Data Mining
COSC530: Computer Systems Organization
COSC533: Cloud and Web Architectures
COSC545: Digital Archeology
ECE517: Reinforcement Learning
ECE553: Computer Networks
Electives
COSC534: Network Security
COSC540: Advanced Software Engineering
COSC559: Human-Computer Interaction
COSC561: Compilers and Runtime Systems
COSC565: Databases and Scripting Languages
COSC566: Software Security
COSC569: Human Factors in Cybersecurity
COSC581: Algorithms
COSC583: Applied Cryptography
Software Engineering
Core (must take both)
COSC540: Advanced Software Engineering
COSC581: Algorithms
Focused (must take a minimum of 4)
COSC526: Data Mining
COSC530: Computer Systems Organization
COSC533: Cloud and Web Architectures
COSC545: Digital Archeology
COSC559: Human-Computer Interaction
COSC561: Compilers and Runtime Systems
COSC565: Databases and Scripting Languages
Electives
COSC522: Machine Learning
COSC523: Artificial Intelligence
COSC524: Natural Language Processing
COSC525: Deep Learning
COSC534: Network Security
COSC566: Software Security
COSC569: Human Factors in Cybersecurity
COSC583: Applied Cryptography
ECE517: Reinforcement Learning
ECE553: Computer Networks