Degree Requirements

Students in the MS program in Mathematics and Statistics emerge with the mathematical and statistical knowledge that underlies methods in data science, machine learning, and artificial intelligence (AI). Our coursework also emphasizes strong computational and hands-on skills. The curriculum of the MS program prepares students for data intensive careers.

The degree requires 10 graduate courses:

  • Core courses in applied mathematics and statistics
    • MATH 5051: Probability Theory/Applications
    • MATH 5052: Deterministic Math Models
    • MATH 5151: Mathematical Statistics
    • MATH 5152: 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 5051 Probability and Applications
  • Math 5052 Deterministic Methods of Applied Mathematics
  • Math 5151 Mathematical Statistics
  • Math 5152 Numerical Methods

Sample of Math/Stat Elective Courses (Choose Five or Six)

  • Math 5011 Partial Differential Equations
  • Math 5007 Mathematics and Climate
  • Math 5014 Optimization
  • Math 5001 Longitudinal Data Analysis
  • Math 5005 Mathematics of Social Network Analysis
  • Math 5041 Introduction to Real Analysis (fall semesters)
  • Math 5200 Mathematical/Statistical Computing
  • Math 5210 Advanced Math/Stat Computing (fall semesters)
  • Math 5070 Introduction to Non-Parametric Statistics 
  • Math 5420 Financial Mathematics
  • Math 5710 Stochastic Simulation
  • Math 5410 Operations Research
  • Math 5600 Bayesian Statistics (spring semesters)
  • Math 5320 Statistical Learning (spring semesters)
  • Math 5520 Time Series (fall semesters)
  • Math 5500 Regression Analysis (fall semester)
  • Math 5530 Applied Multivariate Analysis
  • Math 5330 Data Mining (fall semesters)
  • Math 5060 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
  • Epidemiology
  • Public policy (eg, survey sampling)
  • Economics (eg, econometrics)
  • Machine Translation (Linguistics)
  • Neurosciences
  • Security studies
  • Social network analysis

For the current and upcoming schedule of classes visit the University Registrar’s webpage.

Practical Experience

Internships, consulting, and research experiences are integral parts of the program. Therefore, while this is not a requirement of the degree, each student is strongly 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.