There are two options in the Master program (thesis option and non-thesis option). The non-thesis option requires coursework only, while the thesis option requires a thesis  with your research advisor with less course work. Both options are equally considered for admission without preference or priority. 

For the thesis option, procedure of how to secure an advisor and seek his or her confirmation is the same as that of PhD (see PhD requirements below). Master students may change the degree option at any time during the study.

General Information

The computer science department offers comprehensive Master's degree programs that focus on computer network security, software engineering, web databases, wireless systems, intelligent systems, data mining, parallel and distributed processing, pervasive computing, computer networks, scientific visualization, and algorithms. These research activities support the department’s two major areas of excellence: software engineering and critical infrastructure protection.

 Admission Requirements

• GPA: Minimum undergraduate GPA of 3.0/4.0
• GRE: Verbal + Quantitative 300, Analytical Writing 3.0.
• English proficiency test scores: TOEFL 80, IELTS 6.5, PTE 58, DuoLingo 115.
• Official transcripts required
• Applicants are expected to have strong mathematical skills, competency in a modern programming language,

 Program Requirements

• The Non-Thesis Master's program requires the completion of a total of 31 hours of graduate course work:
  • A minimum of 9 hours of 6000 level CS lecture courses.
  • 15 to 18 hours of 5000 level CS lecture courses.
  • CS 5200 - Algorithms
  • 1 semester of seminar course CS 6010.
  • 3 credit hours of out-of-department courses from the approved list.

 

Core Computer Science Courses (4 hours)

• CompSci 5200: Analysis Of Algorithms (3 credit hours, offered every semester)
• CompSci 6010: Seminar (1 credit hour, offered every semester)

 List of 5000 level Elective Computer Science Courses
(15 to 18 credit hours)

• CompSci 5204: Regression Analysis (3 credit hours, offered every Spring semester)
• CompSci 5205: Real-Time Systems (3 credit hours, offered every Spring semester)
• CompSci 5300: Database Systems (3 credit hours, offered every Fall semester)
• CompSci 5400: Introduction To Artificial Intelligence (3 credit hours, offered every Spring semester)
• CompSci 5401: Evolutionary Computing (3 credit hours, offered every Fall semester)
• CompSci 5402: Introduction to Data Mining (3 credit hours, offered every Spring semester)
• CompSci 5403: Introduction to Robotics (3 credit hours, offered every Fall semester)
• CompSci 5404: Introduction to Computer Vision (3 credit hours, offered every Fall semester)
• CompSci 5405: Java Gui & Visualization (3 credit hours, offered every Spring semester) 
• CompSci 5406: Interactive Computer Graphics (3 credit hours, offered every Spring semester)
• CompSci 5407: Introduction to Virtual Reality (3 credit hours, offered every Fall semester)
• CompSci 5408: Game Theory for Computing (3 credit hours, offered every Fall semester)
• CompSci 5409: Applied Social Network Analysis (3 credit hours, offered every Fall semester)
• CompSci 5420: Introduction to Machine Learning (3 credit hours, offered every Fall semester)
• CompSci 5480: Introduction to Deep Learning (3 credit hours, offered every Spring semester)
• CompSci 5600: Advanced Computer Networks (3 credit hours, offered every Spring semester)
• CompSci 5601: Security Operations & Program Management (3 credit hours, offered every Fall semester)
• CompSci 5602: Introduction to Cryptography (3 credit hours, offered every Fall semester)
• CompSci 5700: Bioinformatics (3 credit hours, offered every Spring semester)
• CompSci 5800: Distributed Computing (3 credit hours, offered every Fall semester)
• CompSci 5802: Introduction to Parallel Programming & Algorithms (3 credit hours, offered every Fall semester)
• CompSci 5803: Principles of High Performance Computer Architecture (3 credit hours, offered every Spring semester)

  List of 6000 level Elective Computer Science Courses
(Minimum 9 credit hours)

• CompSci 6110: Advanced Computer Architecture (3 credit hours, offered every Fall semester)
• CompSci 6202: Markov Decision Processes (3 credit hours, offered every Fall semester)
• CompSci 6204: Applied Graph Theory for Computer Science (3 credit hours, offered every Spring semester)
• CompSci 6304: Cloud Computing and Big Data Management (3 credit hours, offered every Fall semester)
• CompSci 6400: Advanced Topics in Artificial Intelligence (3 credit hours, offered every Fall semester)
• CompSci 6401: Advanced Evolutionary Computing (3 credit hours, offered every Fall semester)
• CompSci 6405: Clustering Algorithms (3 credit hours, offered every Spring semester)
• CompSci 6406: Machine Learning in Computer Vision (3 credit hours, offered every Fall semester)
• CompSci 6407: Internet of Things with Data Science (3 credit hours, offered every Fall semester)
• CompSci 6604: Mobile, IoT and Sensor Computing (3 credit hours, offered every Fall semester)
• CompSci 6800: Distributed Systems Theory and Analysis (3 credit hours, offered every Spring semester)
• CompSci 6801: Topics in Parallel and Distributed Computing (3 credit hours, offered every Spring semester)

   

A full list of course availability and timing can be found here: https://cec.mst.edu/academics/course-availability/

  More Information at

• https://catalog.mst.edu/graduate/graduatedegreeprograms/computerscience/
• https://cs.mst.edu/graduate-degrees/masterofscience/

  

Please note:  During the semester a student will have completed nine hours of graduate credit, the student must formally plan the remainder of their graduate program in consultation with their academic advisor, and submit a Form 1 for approval, first to the department chair and then to the vice provost of graduate education

 

The most important thing for PhD admission is to secure your tentative advisor, even before you formally apply (individual contact as stated below). View the department's faculty directory to review each faculty member's research areas and identify your potential PhD advisor. Contact the faculty member by email with your portfolio (CV, transcript, publication, etc.). If he or she is willing to serve as your PhD advisor, seek confirmation.

Once you have a confirmation, ask for a Recommendation Letter from your potential advisor and specify your potential advisor in your Statement of Purpose to be part of your application package. Please note that the confirmation to serve as your advisor does not mean you’ll be automatically offered a financial assistantship. You need to ask the advisor about the availability of assistantship (see the link to “Graduate Degree Funding”).

You may  apply formally for PhD without this procedure of securing the advisor. We will circulate your application materials to faculty members to see if someone is interested in serving as your advisor. If you are applying for the PhD program without a MS degree (directly from BS) and without securing an advisor, we may still admit you as an MS to be able to convert to PhD within 2-3 semesters (all your credits are then counted toward PhD). During this period, you interact with faculty and demonstrate your aptitude for PhD.

There are three major mechanisms of assistantship for graduate students in Computer Science: Graduate Research Assistants (GRA), Graduate Teaching Assistants (GRA) and Fellowship. There are also Grader positions based on hourly rate wages. Students can be appointed as GRA or GTA by any rate up to 50% FTE (Full-Time Equivalent), which is the maximum part-time employment rate for students (100% represents the full-time job). Monthly wage for 50% FTE is approximately $2,000, which is proportional to the % FTE (e.g. $400 for 10% FTE). The student’s out-of-state tuition is waived if the student is offered at least a 25% FTE or higher.

This assistantship is to support students by contributing to a faculty members’ research. If a GRA is offered, the out-of-state tuition is waived (if higher than 25% FTE) and the monthly wage is provided according to % FTE. Students may contact individual faculty members with materials (CVs, publication, etc.) preferably before application to demonstrate their qualification and competency in the interested research areas. The faculty member may offer a GRA position with admission if the student is qualified and funding is available. Or it may be offered anytime with continued interactions with faculty members during study in the Computer Science program.

GTAs are normally required to teach undergraduate-level laboratory courses. If a GTA is offered, the out-of-state tuition is waived (if higher than 25% FTE) and the monthly wage is provided according to % FTE. The department appoints GTAs every semester. Students apply for the GTA positions several months earlier than the start of class to teach, pass the communication test (non-native speakers only) and are selected by the department. Therefore, new incoming students are typically not eligible for the GTA position during the first semester. Learn more.