Master of Science in Interdisciplinary Data Science-Mathematics

We offer an MS degree in Interdisciplinary Data Science with Major in Mathematics. This MS degree can open up a wealth of career options, enabling graduates to enter a rapidly growing and vibrant sector of the economy with a job market that offers a breadth of opportunities. Our students will acquire skills in high demand by governmental agencies and a range of industries seeking to capitalize on the era of Big Data, Machine Learning and Artificial Intelligence. The program emphasizes the mathematical underpinnings of data science along with data analysis, computation and visualization. Students will learn the mathematical background necessary to understand how and why many machine learning and data analysis algorithms work. A mathematical perspective on the subject leads to a deeper understanding of the methodology and tools of data science and will prepare students to analyze and visualize data, design new tools and adapt existing methods and algorithms when necessary. In addition to a set of core courses, the curriculum will include advanced courses on topics such as principles and foundations of machine learning, graphs and networks, numerical linear algebra, numerical optimization, and topological data analysis.

A bachelor's degree from a regionally accredited U.S. institution, or a comparable degree from an international institution, with a minimum 3.0 (on a 4.0 scale) grade point average (GPA) in all coursework attempted while enrolled as an upper-division undergraduate student working towards a bachelor's degree
Interdisciplinary Data Science
Students must have earned a Bachelor's degree from an accredited institution and have a minimum of 3.0 GPA (B or better average) on the last 60 hours of undergraduate credits. Students must be in good standing at the institution of higher learning last attended.

The following math courses are required prerequisites for admission to the math major.

Calculus I, II, and III (multivariate calculus)
Differential equations
Linear algebra
Probability and Statistics
Students should also have some experience in a programming language, such as C, C++, Fortran, Python, Julia, Matlab.
In addition to the required background, students are recommended to study numerical methods of mathematics, such as a course on Numerical Methods, Numerical Analysis, or Scientific Computing.
As a part of their application, students will provide a statement of intent and CV, or r'sum, and identify three recommenders who will write recommendation letters discussing the student's aptitude for graduate study.
Internet based TOEFL (IBTOEFL): 80
Paper based TOEFL (TOEFL): 550
International English Language Testing System (Academic IELTS): 6.5