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 and Applications
    • MATH 5052: Deterministic Math Models
    • MATH 5151: Statistical Inference & Modeling
    • MATH 5152: Numerical Methods for Data Science
  • Math/stat electives. Math/Stat electives include computational methods: computing with R & Python, cloud computing, regression models, unsupervised learning, financial mathematics, time series, data mining, Bayesian statistics, machine learning, deep learning, mathematics of climate, operations research, optimization, social network analysis, and survey sampling.
  • 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 Theory and Applications
  • Math 5052 Deterministic Methods of Applied Mathematics
  • Math 5151 Statistical Inference & Modeling
  • Math 5152 Numerical Methods for Data Science

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

  • Math 4307 Mathematics of Climate
  • Math 4311 Introduction to Partial Differential Equations
  • Math 4314 Optimization
  • Math 4460 Machine Learning: Theory & Application
  • Math 5060 Survey Sampling
  • Math 5200 Computing with R & Python
  • Math 5210 Cloud Computing
  • Math 5310 Deep Learning
  • Math 5320 Supervised Statistical Learning
  • Math 5330 Data Mining
  • Math 5340 Social Network Analysis
  • Math 5410 Operations Research
  • Math 5420 Financial Mathematics
  • Math 5500 Regression Models
  • Math 5520 Time Series
  • Math 5600 Bayesian Statistics
  • Math 5700 Numerical Optimization for AI & Data Science
  • Math 5710 Unsupervised Learning

Sample of Non-Math Elective (Choose One)

Every student is given the option to take one non-math/stat elective course that counts toward the degree. This must be a graduate level course in an area that extends or makes use of the tools and techniques of mathematics and statistics. Non-math electives must be approved by the Director of Graduate Studies before enrollment. Examples of disciplinary areas are:

  • Biostatistics
  • Computer Science
  • Data Science & Analytics
  • Economics
  • Epidemiology
  • Linguistics
  • Neurosciences
  • Public policy
  • Security Studies

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

Practical Experience

Students are strongly encouraged to engage in internships, consulting, and research experiences while in the program. Organizations offering internship positions include various consulting firms, financial institutions, government agencies, among others that can be found on our Careers page.

International students may engage in off-campus internships and related opportunities after completing a full academic year in the program by obtaining Curricular Practical Training (CPT) approval through the Office of Global Services. International students must enroll in MATH-5925 as part of the requirements for the CPT approval.