Course Curriculum

The thesis option in the Master of Mathematics in Data Science program requires students to complete at least four graduate-level courses. Students who lack sufficient background in computer science or statistics may be required to take CS 600 (Fundamentals of Computer Science for Data Science) or STAT 845 (Statistical Concepts for Data Science), though these do not count toward the four-course requirement. The core curriculum normally includes CS 738 (Data Engineering for Data Science) and STAT 847 (Exploratory Data Analysis), ensuring foundational knowledge in data handling and statistical exploration. Additionally, students must take at least one course from a specified list that includes topics in machine learning, artificial intelligence, optimization, and statistical learning—examples include CS 680, CS 685, CS 686, CS 794, CO 663, CS 786, STAT 840, and STAT 844. The fourth course is typically chosen from an approved list covering areas such as machine learning, optimization, distributed computing, databases, data visualization, and other advanced research topics. Courses like CS 648, CS 654, CS 742, STAT 842, and CS 798 may be selected depending on the student’s focus. Substitutions may be approved by the Data Science Committee, and some 800-level or 900-level courses may count if the content is data science–related and approved by the Graduate Director.