Data Science M.S.
Master of Science - Data Science
Summary
| Code | Title | Hours |
|---|---|---|
| Core Course Requirements | 18 | |
| Additional Courses | 12-18 | |
| Total Hours | 30-36 | |
Degree Specific Credits:
Thesis: 30 credits
Nonthesis: 36 credits
Required Cumulative GPA: 3.0
Notes
- 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.
- After the first year students will take a comprehensive exam on material from M 540, M 561, and M 562. It is structured in two parts, written and preliminary.
Courses
| Code | Title | Hours |
|---|---|---|
| Core Course Requirements | ||
| Complete all of the following courses: | ||
| M 540 | Numerical Methods for Computational & Data Science | 3 |
| M 561 | Advanced Data Science Analytics | 3 |
| M 562 | Advanced Theoretical Big Data Analytics | 3 |
| M 567 | Advanced Big Data Analytics Projects | 3 |
| M 600 | Mathematics Colloquium | 1 |
| M 610 | Graduate Seminar in Applied Mathematics | 2 |
| or STAT 640 | Graduate Seminar in Probability and Statistics | |
| Complete one course in CSCI (see courses below) | 3 | |
| Additional Course Requirements | ||
| Complete additional credit requirements with the following courses: | 4-10 | |
| Data Visualization | ||
| Machine Learning | ||
| Pattern Recognition | ||
| Applications of Mining Big Data | ||
| Applied Parallel Computing Techniques | ||
| Probability Theory | ||
| Mathematical Statistics | ||
| Applied Linear Models | ||
| Applied Multivariate Statistical Analysis | ||
| Theory of Linear Models | ||
| A minimum 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. | 6 | |
| A minimum of 2 research credits is required. A final presentation on a research project must be given in the Applied Math & Statistics seminar. | 2 | |
| Total Hours | 30-36 | |
Minimum Required Grade: C