Students may elect to work toward 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.
Program Requirements
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 Department Head.
- All 3- or 4-credit AMS Department 600-level and 700-level courses (with the exception of EN.553.604 Foundational Methods in Applied Mathematics and research/internship courses), are satisfactory for this requirement.
- Certain courses in other departments are also acceptable, and must be fully approved in advance. At most 3 courses outside the department may be counted toward the 8 (or 10) courses used toward Master's degree requirements.
- JHU courses listed as 2-credit courses (with the exception of research/internship courses) may count only as one-half course. JHU Public Health courses may count only as one-half course. JHU 1-credit courses may not be used.
- Meet either of the following options:
- submit an acceptable research report based on an approved project (see Master’s Student Handbook for details); or
- complete satisfactorily two additional one-semester graduate courses (with the same restrictions listed in section 1) and as approved by the faculty advisor and Department Head.
- 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 3 EN.553.600 Mathematical Modeling and Consulting 4 EN.553.613 Applied Statistics & Data Analysis I 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.553.669 Large-Scale Optimization For Data Science 3 EN.553.681 Numerical Analysis 4 EN.553.683 Numerical Methods for Partial Differential Equations 4 EN.553.688 Computing for Applied Mathematics 3 EN.553.693 Mathematical Image Analysis 4 EN.553.733 Nonparametric Bayesian Statistics 3 EN.553.740 Machine Learning I 3 EN.553.741 Machine Learning II 3 EN.553.753 Three Key Aspects of Climate Change: Energy Transition, Critical Metals and Sustainable Agriculture 3 EN.553.761 Nonlinear Optimization I 3 EN.553.762 Nonlinear Optimization II 3 EN.553.763 Stochastic Search and Optimization 3 EN.553.780 Shape and Differential Geometry 3 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 I Stochastic Processes and Applications to Finance II Monte Carlo Methods Probability Theory I Probability Theory II Stochastic Search and 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 & Data Analysis I Applied Statistics and Data Analysis II Bayesian Statistics Introduction to Data Science Time Series Analysis Computational Molecular Medicine Large-Scale Optimization For Data Science Probabilistic Machine Learning Statistical Theory I Statistical Theory II Nonparametric Bayesian Statistics Topics in Statistical Pattern Recognition High-Dimensional Approximation, Probability, and Statistical Learning Statistical Pattern Recognition Theory & Methods Machine Learning I Machine Learning II Statistical Inference on Graphs Equivariant Machine Learning Iterative Algorithms in Machine Learning: Theory and Applications Statistical Uncertainty Quantification Optimization and Operations Research Mathematical Modeling and Consulting Mathematical Game Theory Optimization in Finance Optimization in Data Science Network Models in Operations Research Introduction to Convexity Large-Scale Optimization For Data Science Nonlinear Optimization I Nonlinear Optimization II Stochastic Search and Optimization Combinatorial Optimization Iterative Algorithms in Machine Learning: Theory and Applications Introduction to Control Theory and Optimal Control Computational and Applied Mathematics Mathematical and Computational Foundations of Data Science Numerical Analysis Numerical Methods for Partial Differential Equations Computing for Applied Mathematics Dynamical Systems Mathematical Biology Mathematical Image Analysis Shape and Differential Geometry Mathematical Foundations of Computational Anatomy Matrix Analysis and Linear Algebra Turbulence Theory Matrix Analysis and Linear Algebra II Discrete Mathematics Select at least one of the following: 1 4 Combinatorial Analysis Graph Theory Combinatorial Optimization Additional Options: Combinatorics & Graph Theory in Computer Science Theory of Computation Intro Algorithms Randomized and Big Data Algorithms Approximation Algorithms Practical Cryptographic Systems -
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.
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Students in the AMS MSE program are strongly encouraged to register in EN.553.801 Department Seminar in at least one semester of their program.
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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 on the department website.