Data Science Master of Science Degree Program

The Data Science Master of Science degree program is designed to provide students with the foundations in all three major fields of Data Science: Topological Data Analysis, Machine Learning, and Statistical Methods. Students will also develop practical working skills in at least one of them. The program requires a minimum of 36 credits. Students are also expected to take a course in computer methods.

Program Requirements - 36 credits 

1. Core Requirement - one course:

• A Mat 502 - Modern Computing for Mathematicians (3 credits)

2. Courses in Topological Data Analysis - two courses:

• A Mat 583 - Topological Data Analysis I (3 credits)
• A Mat 584 - Topological Data Analysis II (3 credits)

3. Courses in Machine Learning - three courses:

• A Mat 590 - Function Theory and Functional Analysis for Applications (3 credits)
• A Mat 591 - Optimization Methods and Nonlinear Programming (3 credits)
• A Mat 592 - Machine Learning (3 credits)

4. Courses in Statistics - three courses:

• A Mat 554 - Introduction to Theory of Statistics I (3 credits)
• A Mat 565 - Applied Statistics (3 credits)
• A Mat 581 - Nonparametric Statistics (3 credits)

5. Practicum Course - choose one course:

• A Mat 585 - Practical methods in topological data analysis (3 credits)
• A Mat 593 - Practical methods in machine learning (3 credits)
• A Mat 582 - Computational Methods in Statistical Inference (3 credits)

6. Elective Courses - choose two courses:

• A Mat 524 - Advanced Linear Algebra (3 credits)
• A Mat 560 - Introduction to Stochastic Processes I (3 credits)
• Additional practicum course

University Capstone Requirement:

The practicum course serves as the capstone experience for the Data Science Master's program. The practicums require comprehensive analysis of data sets along with oral presentations or poster presentations of the results. Students must complete one of the practicum courses with a grade of B or better.