Mathematics, MSc - Scientific Computation & Data Science Track

The Master of Sciences (MSc) with Mathematics Emphasis Program - Scientific Computation & Data Science Track is an interdisciplinary degree that develops foundational knowledge for the modeling, solution and analysis of complex problems and their solution.

Specific areas of study is drawn from applied and computational mathematics, statistics, and computer science.

The admission criteria for the Graduate Program in Mathematics are not limited to the following requirements. The Graduate Committee makes a decision on admission for each applicant based on their application materials and supporting documents.

To be considered for admission, applicants must meet the following requirements:

• Hold a bachelor's degree in mathematics, or another field with substantial mathematics coursework.
• Have introductory computational experience including UCCS course equivalents of Math 2650 and CS 1450.
• Have a minimum overall GPA of 3.0, as well as a minimum GPA of 3.0 in mathematics courses.
• Submit GRE General Test scores, taken within the last 2 years, with a recommended percentile of 80 or higher on the Quantitative Reasoning section.
• International applicants must demonstrate English language proficiency and provide additional documentation, as stated on the Application page.

In certain situations, students with lower GPAs or without a course in the analysis may be admitted as provisional degree students under special circumstances.

Application Deadline: Fall & Spring: Rolling Admission*

*Applications are due one week prior to the term start date

For program coursework, please visit the Academic Catalog.

Requirements for Master of Sciences Degree

General regulations of the Graduate School governing the award of a Master’s degree apply, except as modified below.

• 30-36 semester hours of science or mathematics courses are required.
• All courses must be taken from approved Graduate School faculty members.
• 24 or more hours in science or mathematics must be from courses numbered 5000 or above.
• Because not all courses will be appropriate for all programs, students should first consult with their advisor before enrolling. An academic plan should be completed during the student's first semester.
• Courses at the 3000 and 4000 levels will be accepted toward the degree only with grades of A or B. Courses at the 5000 and 6000 levels will be accepted toward the degree with grades of A, B, or C. Students must have a B average in all courses taken subsequent to admission to the program, including courses not actually required for the degree.

Mathematics Program Requirements for Scientific Computation & Data Science Track

• 30 semester hours of science or mathematics courses are required.
• Courses at the 3000 and 4000 levels will be accepted toward the degree only with grades of A or B. Courses at the 5000 and 6000 levels will be accepted toward the degree with grades of A, B, or C. Students must have a B average in all courses taken subsequent to admission to the program, including courses not actually required for the degree.
• Deviations from the standard curriculum should be well-motivated and must be approved by the Graduate Chair.
• Students must complete at least two in-class final projects which will be offered in some of these courses. These may also include projects by a small group of students working together.
• A project report portfolio must be submitted to the Graduate Chair before graduation.

Math Courses (6 courses, 18 credit hours)
• MATH 5650 (Numerical Analysis)
• MATH 5420 (Optimization)
• MATH 5470 (Methods in Applied Mathematics)
• MATH 5670 (Scientific Computation I)

Any 1 from the following 2 courses:
• MATH 5680 (Scientific Computation II)
• MATH 5390 (Numerical Linear Algebra)

Any 1 from the following 2 courses:
• MATH 5480 (Mathematical Modeling)
• MATH 5850 (Stochastic Modeling)

CS Courses (4 courses, 12 credit hours)

• CS 3080 (Python Programming)

Any 3 from the following 4 courses:
• CS 5820 (Artificial Intelligence)
• CS 5860 (Machine Learning)
• CS 5870 (Intro to Artificial Neural Networks)
• CS 5420 (Database Systems I)