COURSE CURRICULUM

The Master of Science in Applied Mathematics at the University of Colorado Denver requires a minimum of 30 semester credit hours and is structured to ensure both strong theoretical foundations and applied specialization. At least 24 of these credit hours must be completed in mathematics courses at the 5000 level or above, and all students are required to take Math 5718 (Applied Linear Algebra) along with either Math 5070 (Applied Analysis) or Math 6131 (Real Analysis), ensuring a core grounding in advanced mathematical methods. Students may either complete a general MS degree or choose a concentration in areas such as applied probability and uncertainty quantification, applied statistics, data science, discrete mathematics, mathematics of engineering and science, numerical analysis, or operations research, allowing for specialization aligned with career or research interests. Up to six credit hours may be taken outside the Department of Mathematical and Statistical Sciences at the 4000 level or higher, provided they are approved by the advisor and graduate committee, while courses from other universities must meet the 5000-level graduate standard and also receive approval. Academic performance requirements include maintaining a minimum 3.0 GPA, and only courses with a grade of B- or higher may count toward the degree, although lower grades still affect the overall GPA. A maximum of nine credit hours of transfer or non-degree coursework may be applied toward the degree, subject to formal approval by the Graduate Committee and the College of Liberal Arts and Sciences Dean’s Office. Certain lower-level or restricted mathematics courses are excluded from degree applicability. In addition to coursework, all students must pass a final oral examination and complete a written project, demonstrating mastery of applied mathematical concepts and research skills. Students have five years from admission to complete all requirements, and those pursuing a thesis option must register for MATH 5950, with 4–6 credit hours counting toward the degree. Overall, the program combines rigorous coursework, specialization flexibility, and research-based evaluation to prepare students for advanced careers in data science, analytics, engineering, and academic research.