MS in Health Data Science - On-Campus

Below, you will find detailed information about the Master of Science in Health Data Science 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 Health Data Science On-Campus program, view the full 2024-2025 academic calendar. You can also view the 2025-2026 calendar here.

• Demonstrate core competency in  bioinformatics, biostatistics, and epidemiology
• Demonstrate skills required to pursue careers in data science as applied in biomedical settings, including:
• Ability to apply statistical methodologies on large datasets (“big data”)
• Correct use of statistical models that consider data design, encoding, and organization
• Ability to develop and apply algorithms for processing and analysis of biomedical data
• Ability to apply machine learning algorithms
• Ability to produce effective data visualizations which narrate central points for presentation and reporting. 

The core coursework of the Master of Science degree in Health Data Science builds a firm foundation in programming, biostatistics, and classification algorithms including machine learning, and builds the skills to apply these methods with real world data.

The Health Data Science curriculum also includes an optional 3-unit capstone that challenges students to refine their data science skills while working on a real-world applied data project. Beyond showcasing technical knowledge, the capstone experience also provides the 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.