The Data Science MS degree with major in Mathematics requires 30 credit hours of coursework. There is a core curriculum of 18 credit hours all Data Science students take. In addition, students who major in Mathematics take at least 9 credit hours from mathematics. The rest of the credit hours can be taken from the participating departments.

Students interested in taking an elective class that is not listed here should contact Dr. Ökten.

All data science graduate students must pass the following courses:

**Core Requirements:** (18 credit hours)

- MAP 5196 - Mathematics for Data Science (3 Hrs, Fall)
- CIS 5930 - Introduction to Data Science (3 Hrs, Fall)
- STA 5207 - Applied Regression Methods (3 Hrs, Fall)
- STA 5910 - Professional Development Seminar (1 Hr, Fall)
- STA 5635 - Machine Learning (3 Hrs, Spring)
- CAP 5771 - Data Mining (3 Hrs, Spring)
- PHI 5699 - Data Ethics (2 Hrs, Spring)

**Math Major Requirements:** (At least 9 credit hours. Any substitutions to the
courses below require the approval of Associate Chair for Graduate Studies.)

- MAD 5XXX - Principles and Foundations of Machine Learning (3 Hrs, Fall)
- MAD 5XXX - Numerical Linear Algebra (3 hrs, Spring)
- MAD 5420 - Numerical Optimization (3 Hrs, Fall)
- MAD 5306 - Graphs and Networks (3 Hrs, Fall)
- MAP 5345 - Elementary Partial Differential Equations I (3 Hrs, Fall or Spring)
- MTG 5356 - Topological Data Analysis (3 Hrs, Fall)

**After satisfying the core and math major requirements, students who need additional credit hours can take more
courses from the above list or the electives below:**

- CAP 5769 - Advanced Topics in Data Science (3 hrs)
- ISC 5318 - High Performance Computing (3 hrs)
- STA 5326 - Distribution Theory and Inference (3 hrs)
- STA 5166 - Statistics in Application I (3 hrs)
- STA 5167 - Statistics in Application II (3 hrs)
- MAD 5403 - Foundations of Computational Mathematics (3 hrs)
- MAD 5404 - Foundations of Computational Mathematics II (3 hrs)

A schedule to complete the degree in three semesters:

Fall Year 1 | Spring Year 1 | Fall Year 2 |
---|---|---|

Mathematics for Data Science (3) MAP 5196 |
Machine Learning (3) STA 5635 |
(Math)-elective (3) |

Introduction to Data Science (3) CIP 5930 |
Data Mining (3) CAP 5771 |
(Math)-elective (3) |

Applied Regression Methods (3) STA 5207 |
Data Ethics (2) PHI 5699 |
(Math)-elective (3) |

Professional Development Seminar (1) STA 5910 |
(Math)-elective (3) |