Computer Science (M.S.)

Master of Science, Computer Science Plan A (thesis): Total 30 credit hours

Master of Science, Computer Science Plan B (non-thesis): Total 30 credit hours

Elective Work: Plan A takes 6 credit hours, Plan B takes 12 credit hours from the following:

• CSC 501 - Rich Internet Applications 4 cr hrs
• CSC 505 - Business Analytics Fundamentals 3 cr hrs
• CSC 511 - Simulation 3 cr hrs
• CSC 525 - High Performance Computing 3 cr hrs
• CSC 535 - Human Factors in Computer Systems 3 cr hrs
• CSC 545 - Introduction to Theory of Computation (C) 3 cr hrs
• CSC 547 - Artificial Intelligence (C) 3 cr hrs
• CSC 555 - Algorithms 4 cr hrs
• CSC 556 - Operating Systems 3 cr hrs
• CSC 561 - Programming Languages 3 cr hrs
• CSC 570 - Software Engineering 3 cr hrs
• CSC 571 - Numerical Analysis I 3 cr hrs
• CSC 575 - Operations Research 3 cr hrs
• CSC 581 - Systems Analysis 3 cr hrs
• CSC 584 - Database Management Systems 3 cr hrs
• CSC 586 - Data Mining 3 cr hrs
• CSC 591 - Independent Study 1 to 4 cr hrs
• CSC 592 - Topics (C) 1 to 3 cr hrs
• CSC 594 - Internship (C) 1-3 cr hrs
• CSC 5XX, 6XX, or 7XX-Graduate coursework in Computer Science with the department’s approval

Artificial Intelligence Specialization optional (15 credit hours)

Start date Fall 2021

• Select 9 credit hours from the following core courses:

• CSC 721 - Distributed Systems 3 cr hrs
• CSC 722 - Machine Learning Fundamentals 3 cr hrs
• CSC 752 - Computer Vision (C) 3 cr hrs
• CSC 761 - Advanced Artificial Intelligence (C) 3 cr hrs
• CSC 785 - Information Storage and Retrieval 3 cr hrs

•  

• Select 6 credit hours from the following elective courses:

• CSC 525 - High Performance Computing 3 cr hrs
• CSC 557 - Data Analysis, Decision Making, and Visualization 3 cr hrs
• CSC 586 - Data Mining 3 cr hrs
• CSC 588 - Pattern Recognition & Machine Learning 3 cr hrs
• STAT 580 - Applied Statistics 3 cr hrs
• STAT 581 - Probability and Statistics 3 cr hrs

Informatics Specialization optional (12 credit hours plus 3 credit hours specified in the core)

 Plan A students must choose a thesis topic in the informatics area.

• Core courses:

• CSC 525 - High Performance Computing 3 cr hrs OR
• CSC 6XX/7XX Elective course with High Performance Computing content

•  

• CSC 586 - Data Mining 3 cr hrs OR
• CSC 6XX/7XX Elective course with Advanced Data Mining content 3 cr hrs

•  

• CSC 785 - Information Storage and Retrieval 3 cr hrs * OR
• CSC 6XX/7XX Elective course with Advanced Information Storage and Retrieval content 3 cr hrs

•  

• Select one of the following 3 credit hours elective-Graduate coursework in Computer Science or in another discipline with the department’s approval:

• CSC 601 - Introduction to Bioinformatics 3 cr hrs
• CSC 6XX/7XX Elective course with BioInformatics content 3 cr hrs
• CPHD 601 - Introduction to Bioinformatics 3 cr hrs

•  

• Statistics course (3 credit hours):

• BIOL 520 - Introduction to Biostatistics and Computational Biology 3 cr hrs
• BIOL 520L - Introduction to Biostatistics and Computational Biology 0 cr hrs

• Or

  Other graduate-level statistics class approved by the Computer Science department is acceptable also.

•  

• * Course counts towards core coursework.

Accelerated Program Master of Science, Computer Science: BS/MS

Up to 9 credits applied toward the B.S. program may be used to satisfy graduate credits.  The following restrictions apply:

1. The courses must be taken at the 500/600 level as an undergraduate. Courses taken at the 500/600-level can be applied to both the B.S. and M.S. degrees. Dual-listed courses must be taken at the 500-level.
2. The student must apply to, and be admitted to, the accelerated program prior to taking courses to be credited toward the accelerated program.
3. No courses taken prior to admission to the accelerated program may be counted toward an accelerated graduate degree. No exceptions to this policy will be approved.
4. Courses that are “double counted” must be approved by the department chair for inclusion in the program of study prior to registration for the course or the credits will not be applied toward the accelerated graduate degree. No exceptions to this policy will be approved.
5. Only courses taken at the student’s home institution are eligible for dual credit. No transferred courses from other institutions will be allowed to count toward the accelerated master’s degree.
6. To fulfill the undergraduate required courses, graduate courses on the same topic areas must be taken.

Student Learning Outcomes

1. The students  will demonstrate the ability to research a topic of substantial complexity, and evaluate currently known approaches.  (ABET a,b,c,j,k)
2. The students will demonstrate the ability to organize technical material and communicate it orally. (ABET f)

State Authorization - It Matters Where You Live

If your learning placement course (internship, externship, clinical, rotation, practicum, independent study, study away, etc.) or your online course will be taken outside South Dakota, please reference the State Authorization webpage.

State Authorization