The MS Degree
From a rich and diverse curriculum, our students emerge with key practical skills in areas of national priority. In addition to the career-enabling aspects of the degree, our students also develop a love and appreciation for the beauty and excitement of the world of applied mathematics and statistics. The degree requires 10 graduate courses.
- 4 core courses in applied mathematics and statistics consisting of probability (MATH 501: Probability Theory/Applications), applied mathematics (MATH 502: Deterministic Math Models), mathematical statistics (MATH 503: Mathematical Statistics), and numerical mathematics (MATH 504: Numerical Methods).
- Math/stat electives. Math/Stat electives include computational science (Matlab, SAS, R, and cloud introduction), regression analysis, stochastic processes, financial mathematics, time series, data mining, Bayesian statistics, linear programming, machine learning, data analytics, mathematics of climate, sparse sampling and representation, social network analysis, survey sampling, and cloud computing.
- Every student is also permitted to take one non-math/stat elective. Some examples of non-math/stat electives are biostatistics, computer science, econometrics, public policy surveys and computational neuroscience.
Graduate degree requirements consist of 30 credits of graduate level courses (usually 10 courses) and a minimum GPA of 3.0 to graduate. There is no thesis option. Course requirements are as follows:
Core Courses (Required) — offered both Fall and Spring semesters
- Math 501 Probability and Applications
- Math 502 Deterministic Methods of Applied Mathematics
- Math 503 Mathematical Statistics
- Math 504 Numerical Methods
Sample of Math/Stat Elective Courses (Choose Five or Six)
- Math 401 Partial Differential Equations
- Math 412 Mathematics and Climate
- Math 425 Optimization
- Math 426 Longitudinal Data Analysis
- Math 442 Mathematics of Social Network Analysis
- Math 509 Introduction to Real Analysis (fall semesters)
- Math 510 Mathematical/Statistical Computing
- Math 511 Advanced Math/Stat Computing (fall semesters)
- Math 513 Introduction to Non-Parametric Statistics
- Math 605 Financial Mathematics
- Math 611 Stochastic Simulation
- Math 615 Operations Research
- Math 623 Sparse Representation and Random Sampling
- Math 640 Bayesian Statistics (spring semesters)
- Math 642 Statistical Learning (spring semesters)
- Math 645 Time Series (fall semesters)
- Math 651 Regression Analysis (fall semester)
- Math 652 Applied Multivariate Analysis
- Math 656 Data Mining (fall semesters)
- Math 657 Categorical Data Analysis
- Math 658 Survey Sampling (alternate summers)
Sample of Non-Math Elective (Choose One)
Every student is given the option (that counts toward the degree) to take a non-math/stat elective course in a scientific area that extends or makes use of the tools and techniques of mathematics and statistics. Examples of such elective courses are as follows:
- Computer science (graduate level course, please check prerequisites)
- Biostatistics and Epidemiology
- Public policy (eg, survey sampling)
- Economics (eg, econometrics)
- Machine Translation (Linguistics)
- Security studies
- Social network analysis
For the current and upcoming schedule of classes visit the University Registrar’s webpage at http://registrar.georgetown.edu/.
Internships, consulting, and research experiences are integral parts of the program. Therefore, each student is encouraged to participate in such an activity. This can be fulfilled through an internship, a special project in a graduate course or a research collaboration with Georgetown faculty.
Organizations offering internship positions include the US Census Bureau, Bureau of Economic Analysis, Department of Energy, Department of Justice, Department of Agriculture, Controller of the Currency, Federal Reserve Board, Federal Aviation Administration, various financial consulting firms, DecisionQ Corp, NASA, Army National Guard, Center for Advanced Defense Studies, Insight Policy Research, Elder Research and Fannie Mae.