MS in Epidemiology – On-Campus

Below, you will find detailed information about the Master of Science in Epidemiology program, including curriculum structure, experiential learning opportunities, the academic calendar, and course descriptions. Use the links below to navigate quickly:

• Year at a Glance
• Academic Calendar
• Core Coursework
• Learning Objectives

• The academic year is divided into four terms.
• Throughout the academic year, students will take core and elective courses, with core coursework emphasizing topics in theoretical and applied epidemiology, bioinformatics, and statistical modeling. Toward the second half of the year, students will wrap up remaining electives and complete capstone coursework.
• Students need to complete a minimum of 15 units to graduate; core courses are required, and students can choose from several elective options to complete their degree.
• The optional capstone course will take place in summer, with a preparatory course in Winter.
• Students take classes full-time, with approximately 55 hours per week combined coursework and class-time.

To see the days and times that courses are offered by term for the MS in Epidemiology - On-Campus program, view the full 2024-2025 academic calendar. You can also view the 2025-2026 calendar here.

• Core competency in bioinformatics and biostatistics, and epidemiology.
• Competency in foundational biostatistics for complex statistical modeling of public health data.
• The ability to adapt and interpret epidemiological study designs for primary and post-hoc outcomes.
• The ability to identify and validate molecular markers for use in clinical research.
• Working knowledge of clinical, global, molecular, and environmental exposure epidemiology.

The Epidemiology curriculum also includes an optional 3-unit capstone that challenges students to refine their knowledge and skills while working on a real-world applied data project. Beyond showcasing technical knowledge, the capstone experience also provides training in critical skills needed for professional success, such as scientific writing, presentation skills, and the ability to translate data science findings to non-data science stakeholders.