Master of Science in Data Science

The Data Science MS degree is an interdisciplinary program offered by the Department of Computer Science at University of North Dakota.

Admission Requirements

  1. Bachelor’s degree, normally in Computer Science.
  2. Overall undergraduate GPA of at least 2.85.
  3. Graduate Record Examination General Test or an undergraduate degree from a CSAB/ABET accredited degree program in Computer Science.
  4. Satisfy the School of Graduate Studies’ English Language Proficiency requirements as published in the graduate catalog.
  5. International applicants who have received their bachelor’s or master’s degree in the United States or English-speaking Canada are not required to submit the TOEFL or IELTS.

Applicants with a background in mathematics, science, or engineering will also be considered if they are adequately prepared in the field of computer science.

Students who do not meet all of these prerequisites may be admitted in Qualified or Provisional status with the obligation of meeting the remaining requirements early in their graduate study.

Degree requirements

Students seeking the Master of Science degree DS must satisfy all general requirements set forth by the School of Graduate Studies as well as particular requirements set forth by the Computer Science Department.  More specifically, to obtain the MS in Data Science, students must complete 30 hours depending on the tracks.

There are two tracks:

  1. Thesis track, which will be offered both online and on Campus. Students in thesis track are required to write and defend their theses.
  2. Non-thesis, which will being offered ONLY online. Students in non-thesis track are required to fully develop, implement, and present a capstone project supervised by a graduate faculty member. The presentation, which is considered as a final oral examination, must be publicly presented to the faculty. Both tracks are required to take the same number of courses. The difference between the tracks are in the project, namely, capstone, and thesis. Both capstone and thesis are given the same weight. 

Required Core Courses - 9 credits:

CSCI 513Advanced Database Systems3
CSCI 515Data Engineering and Management3
CSCI 532High Performance Computing and Paradigms3

All students are required to obtain interdisciplinary analytics training. This requirement may be met by taking 3 courses from one of the analytics clusters.

Non-Thesis Option (30 credit hours):

1. The core of required courses (9 credits).

2. Three elective courses (9 credits). Only the following courses may count towards the electives:

CSCI 457Electronic Commerce Systems3
CSCI 543Machine Learning3
CSCI 544Soft Computing: Computational Intelligence I3
CSCI 547Scientific Visualization3
CSCI 551Security for Cloud Computing3

3. CSCI 994 Capstone Project (3 credits).

4. Presentation of the Capstone Project results (CSCI 994 Capstone Project) including an oral presentation and written report (in a format suitable for publication) to the Faculty Advisory Committee, and interested faculty and students.

Thesis Option (30 credit hours):

1. The core of required courses (9 credits).

2. Two elective courses (6 credits). Only the following courses may count towards the electives:

CSCI 457Electronic Commerce Systems3
CSCI 543Machine Learning3
CSCI 544Soft Computing: Computational Intelligence I3
CSCI 547Scientific Visualization3
CSCI 551Security for Cloud Computing3

3. Analytics courses (9 credits).

4. Thesis (6 credits).

5. A final oral examination, which includes a defense of the thesis to the Faculty Advisory Committee, and interested faculty and students.

Analytics clusters:

1. Business Analytics cluster (9 credit hours):

ECON 506Econometrics3
Select two of the following:
ECON 411Economic Forecasting3
ECON 510Topics in Applied Econometrics3
ECON 534Applied Economic Analysis3
ECON 545Applied Public Economics3

2. Educational Foundations and Research cluster (9 credit hours):

EFR 513Large Dataset Management and Analysis3
EFR 530Learning Analytics3
EFR 535Data Analytics and Visualization with R3

3. Behavioral Data Analytics cluster (9 credit hours):

PSYC 540Foundations of Behavioral Data Analytics3
PSYC 541Advanced Univariate Statistics3
PSYC 542Multivariate Statistics for Psychology3

Office of the Registrar

Tel: 701.777.2711
1.800.CALL.UND
Fax: 701.777.2696

Twamley Hall Room 201
264 Centennial Drive Stop 8382
Grand Forks, ND 58202-8382