M.S. in Applied Statistics Curriculum

Overview

Built for a data-driven economy, this 30-credit program turns statistical knowledge into market-ready skills. Five rigorous core courses lay the groundwork— from computational statistics and advanced data analysis to multivariate analysis and nonparametric statistical learning.

From there, choose your path: the General Track offers maximum flexibility with 15 elective credits to craft your unique expertise, while our specialized tracks dive deep into high-value specializations. Financial Statistics students master financial time series for economic predictions, stochastic calculus for derivatives pricing, and stochastic processes for forecasting, risk assessment, and portfolio optimization. Biostatistics students become fluent in clinical trial design, survival analysis methods that inform FDA approvals, and bioinformatics techniques that drive breakthroughs in personalized medicine.  

Every course integrates computer programing (Python, R, etc.) with real-world datasets, ensuring you graduate with both theoretical mastery and the computational fluency that make you a strong candidate in today’s job market. The program culminates with a capstone project that allows you to develop industry collaborations, publish research, or to launch a startup.  

Degree Requirements 

To earn the M.S. in Applied Statistics, all students complete 15 credits of required foundational courses, as well as 15 credits of elective courses. All courses are 3 credits, unless otherwise noted.  

Download Course Descriptions

Core Requirements (5 courses / 15 credits)

Students must complete five of the following courses:

• Computational Statistics and Probability
• Multivariate Analysis
• Non-parametric Statistical Learning
• Data Acquisition and Management
• Capstone in Applied Statistics 

Electives: General Track (5 courses / 15 credits)

Students must complete five of the following courses:

• Introduction to Biostatistics
• Time Series Analysis
• Machine Learning
• Predictive Models
• Bayesian Methods
• Special Topics (1-3 credits)  
• Independent Study (1-3 credits)  
• Internship (1-3 credits)*  

Electives: Financial Statistics Track (5 courses / 15 credits)

Students must complete five of the following courses:

• Mathematics of Finance
• Time Series Analysis  
• Stochastic Processes   
• Stochastic Calculus   
• Machine Learning or Predictive Models or Bayesian Methods  

Electives: Biostatistics Track (5 courses / 15 credits)

Students must complete five of the following courses:

• Introduction to Biostatistics
• Advanced Biostatistics
• Statistics in Trials  
• Bioinformatics
• Machine Learning or Predictive Models or Bayesian Methods 

Note:  Electives offerings will vary each semester. Therefore, some choices will not be available for a particular cohort.  

*Internship can be taken as an elective beginning in the summer semester.