M.S. in Applied Statistics Curriculum

Explore Applied Stats Course Descriptions and Degree Requirements

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.

Course Descriptions

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. 

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.

Curriculum Overview

The 30-credit M.S. in Applied Statistics turns rigorous statistical theory into market-ready expertise, preparing graduates to solve high-stakes problems in finance, medicine, technology, and AI. Core courses build mastery in Computational Statistics and Probability, Multivariate Analysis, Non-Parametric Statistical Learning, and Data Acquisition and Management — developing the analytical depth that separates statisticians who understand their models from those who simply run them.

Students choose from three tracks: a General Track with electives in Machine Learning, Bayesian Methods, and Time Series Analysis; a Financial Statistics Track in Stochastic Calculus and Mathematics of Finance; and a Biostatistics Track covering Clinical Trial Design, Bioinformatics, and Survival Analysis. The program culminates in a capstone integrating industry collaboration, original research, or startup development. It can be completed full-time in as little as 15 months or part-time at a pace that works for you.

Course Descriptions

How You Learn

At the Katz School, you learn the way real innovators work — identifying problems, then designing, building, testing, and improving solutions across a multi-semester project. In the Applied Statistics program, that means working alongside faculty who are active researchers, applying statistical modeling, machine learning, and computational methods to real datasets in finance, healthcare, and technology. You graduate with the theoretical mastery and computational fluency to make an immediate impact in today's data-driven economy.

How You Learn

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