Students may elect to work toward either the master of arts (M.A.) degree or the master of science in engineering (M.S.E.) degree in applied mathematics and statistics. All master’s degrees in the Department of Applied Mathematics and Statistics ordinarily require a minimum of two semesters of registration as a full-time resident graduate student.
To obtain departmental certification for the master’s degree in Applied Mathematics and Statistics, the student must:
- Complete satisfactorily at least eight one-semester courses of graduate work in a coherent program approved by the faculty advisor. All 600-level and 700-level courses (with the exception of seminar and research courses), are satisfactory for this requirement. Certain courses in other departments are also acceptable, and must be approved in advance. At most 3 courses outside the department may be counted toward the Master's degree requirements. WSE courses listed as 1- or 2-credit courses count only as one-half course. Approved KSAS graduate courses count as one-half course if the number of meeting hours per week is 1 or 2 and count as a full course otherwise.
- Meet either of the following options:
- submit an acceptable research report based on an approved project; or
- complete satisfactorily two additional one-semester graduate courses, as approved by the faculty advisor and Chair.
- Satisfy the computing requirement by receiving a grade of B- or better in one of the following courses:
Course List Code Title Credits AS.110.445 Mathematical and Computational Foundations of Data Science 4 EN.553.600 Mathematical Modeling and Consulting 4 EN.553.613 Applied Statistics and Data Analysis 4 EN.553.632 Bayesian Statistics 3 EN.553.633 Monte Carlo Methods 4 EN.553.636 Introduction to Data Science 4 EN.553.650 Computational Molecular Medicine 4 EN.553.681 Numerical Analysis 4 EN.553.688 Computing for Applied Mathematics 3 EN.553.693 Mathematical Image Analysis 3 EN.553.740 Machine Learning I 3 EN.553.743 Graphical Models EN.553.753 Commodity Markets and Trade Finance 4 EN.553.761 Nonlinear Optimization I 3 EN.553.762 Nonlinear Optimization II 3 EN.553.763 Stochastic Search & Optimization 3 EN.553.765 Convex Optimization 3 EN.553.780 Shape and Differential Geometry EN.601.675 Machine Learning 3 EN.601.682 Machine Learning: Deep Learning 4
- Complete an area of focus by taking three courses in one of the following areas. A list of courses that can be counted toward each area of focus will be maintained and updated every year. Some courses from other departments can be eligible to count toward the area of focus. They can be used within the three-course limit specified in point 1, above. This list of courses is based on recent offerings. Not all classes are available every year and substitute classes may be accepted if approved by the advisor and the Academic Affairs Committee.
Course List Code Title Credits Select three courses in one of the following areas: Probability Theory Mathematical and Computational Foundations of Data Science Introduction to Stochastic Processes Stochastic Processes and Applications to Finance Stochastic Processes and Applications to Finance II Monte Carlo Methods Probability Theory I Probability Theory II Introduction to Stochastic Calculus Stochastic Search & Optimization Modeling, Simulation, and Monte Carlo Statistics and Statistical Learning Mathematical and Computational Foundations of Data Science Research and Design in Applied Mathematics: Data Mining Applied Statistics and Data Analysis Applied Statistics and Data Analysis II Bayesian Statistics Introduction to Data Science Time Series Analysis Computational Molecular Medicine Statistical Theory Statistical Theory II Advanced Topics in Bayesian Statistics Distribution-free statistics and Resampling Methods Statistical Pattern Recognition Theory & Methods Machine Learning I Statistical Inference on Graphs Statistical Uncertainty Quantification Optimization and Operations Research Mathematical Modeling and Consulting Optimization in Finance Mathematical Game Theory Network Models in Operations Research Introduction to Convexity Deep Learning in Discrete Optimization Nonlinear Optimization I Nonlinear Optimization II Stochastic Search & Optimization Convex Optimization Combinatorial Optimization Topics in Discrete Optimization Introduction to Control Theory and Optimal Control Computational and Applied Mathematics Mathematical and Computational Foundations of Data Science Numerical Analysis Computing for Applied Mathematics Mathematical Biology Mathematical Image AnalysisEN.553.780 Shape and Differential Geometry Mathematical Foundations of Computational Anatomy Matrix Analysis and Linear Algebra Turbulence TheoryEN.553.795 Advanced Parameterization in Science and Engineering Discrete MathematicsSelect at least one of the following: 1 Combinatorial Analysis Graph Theory Combinatorial OptimizationAdditional Options: Theory of Computation Intro Algorithms Randomized and Big Data Algorithms Approximation Algorithms
- Complete training on the responsible and ethical conduct of research. Please see WSE Policy on the Responsible Conduct of Research.
- Complete training on academic ethics.
The Discrete Mathematics area of focus requires a minimum of one Applied Mathematics and Statistics course (listed in the first section), but the other two courses may include other listed Applied Mathematics and Statistics offerings or the listed Computer Science offerings. The Computer Science courses can be used within the three-course limit specified in point 1, above.
An overall GPA of 3.0 must be maintained in courses used to meet the program requirements. At most two course grades of C or C+ are allowed to be used and the rest of the course grades must be B- or better.
Each candidate for the master’s degree must submit to the department for approval a written program stating how they plan to meet their degree requirements. This should be done early in the first semester of residence.
Doctoral students in other departments may concurrently undertake a master’s program in Applied Mathematics and Statistics with the permission of the AMS department through an application review. Application information is available in the department office.