Master of Science in Data Science

The Master of Science Program aims to provide students with a background in the foundations and principles of data science and the practical application of data science methods. The Department of Mathematical Sciences M.S. Program corresponds to the traditional mathematics option of the department's M.A. in Mathematics option. Candidates for admission to the Master of Science degree program should have an undergraduate mathematics minor and at least one course in computer programming. In addition to meeting the general Graduate School requirements, as set forth in the University's Graduate Bulletin, the M.S. candidate must fulfill departmental requirements as outlined below. Each student will have an advisor who must approve the individual's course of study. The department's Graduate Committee initially assigns an advisor. Students may apply to the Committee for a change of advisor.

Course Requirements

The Graduate School minimum course requirements for a master's degree are 30 graduate credits with a thesis or 36 graduate credits without a thesis. At least half of the credits required for a degree (excluding a combined total of 10 credits for thesis or research) will be at the 500 or 600 level. (In no case, however, will this rule require more than 18 credits of 500- or 600-level work.) To apply this rule to the course of study, subtract the number of thesis and research credits completed (up to 10 only) from the minimum number of credits required for the degree. Half of the remaining credits must be in courses at the 500 or 600 level. The student and the student's advisor design a program of studies for each student. Each year the student must complete (or update) an advisor-approved Program of Studies form which is to be kept on file in the Mathematics office. A revised form must be filed if there are any changes to the student's program during the year.

The program of study must include the following:

  • Depth requirement in Data Science: M561 (Advanced Data Science Analytics), M562 (Advanced Theoretical Data Analytics), M567 (Advanced Data Analytics Projects), M540 (Numerical Linear Algebra), and one 3 credit CSCI course from the list below.
  • 2 credits of M600/M640, the co-convening Applied Mathematics and Statistics seminar
  • 1 credit of Colloquium
The remaining course credits in Statistics and Computer Science (CSCI) are chosen from the following list.
  • Statistics: S421, S422, S542, S543, S545
  • CSCI: CSCI 444 (Data Visualization), CSCI 547 (Machine Learning), CSCI 548 (Pattern Recognition), CSCI 564 (Applications of Mining Big Data), CSCI 580 (Applied Parallel Computing Techniques)
  • A maximum of 6 credits of electives drawn from courses offered by Mathematical Sciences, CSCI, and the School of Business Administration. These courses must be approved by the advisor.

After the first year students will take a comprehensive exam on material from M561, M561, and M540. It is structured in two parts, written and computational. More detail follows.

A minimum of 2 research credits is required. A final presentation on a research project must be given the Applied Math & Statistics seminar.

Student Progress and Financial Support

Financial support is not available in the form of teaching assistantships (TA). Support may be available in the form of research assistantships (RA), pre-doctoral associates (PDA), or graduate student instructorships (GSI). However, the availability of support is limited and there should be no expectation on the part of the applicant regarding support.