MS in Health Data Science - On-Campus

Dartmouth's MS in Health Data Science is offered in two formats:

• On-campus: A full-time, immersive experience in Hanover, New Hampshire, featuring in-person learning, direct faculty interaction, and access to campus resources.
• Online: Designed for working professionals, this format provides flexibility with asynchronous coursework, allowing students to complete the program remotely over 2–3 years.

Both formats lead to the same degree and cover the same rigorous curriculum. For a detailed comparison, view this chart.

Before matriculation, students may request to switch formats by emailing the Admissions office. However, after matriculation, switching is not typically allowed due to differences in structure and scheduling. In exceptional cases (e.g., personal or professional circumstances), students may request a change, though this may require transferring to a new cohort or reapplying.

Students gain hands-on experience with essential tools for healthcare data analysis, including:

• Programming Languages: Python (data analysis, machine learning), R (statistical computing), SQL (database management), SAS (clinical data analysis).
• Software & Tools: Jupyter Notebooks, RStudio, Tableau, Power BI, Hadoop, Spark (for big data processing).

While prior health data science experience is not necessary, competitive applicants typically demonstrate proficiency in data science concepts and analytical techniques. Calculus, computer programming, and statistics coursework is recommended.

Yes. Dartmouth’s program emphasizes real-world applications, offering:

• Opportunities to collaborate with faculty on current research projects.
• Opportunities to connect with industry experts through guest lectures, workshops, and potential research projects via independent studies. ​​​​​​​​​​​​​​
• A capstone course designed for students to tackle real-world challenges or research gaps within the health sciences.

Dartmouth MS in Health Data Science alumni work in biotech, pharmaceuticals, healthcare, public health, and government. Common roles include:

• Data Scientist
• Biostatistician
• Health Informatics Specialist
• Machine Learning Engineer
• Clinical Data Manager
• Public Health Analyst

Graduates use data-driven insights to improve patient outcomes, optimize healthcare operations, and inform policy decisions.

The capstone is a classroom based, team project, guided by faculty. Industry and academic partners will pitch "big challenges" to the class, and students form teams to attack one of the challenges. Students are mentored through the process of researching the problem, formulating an approach and justifying their approach, building project management and division of labor plans, building and testing data management and statistial analysis plans, compiling the results and reporting back to the class with the project findings. Students will present a poster of their work at a ceremony at the end of the class.

This program is ideal for individuals with backgrounds in mathematics, statistics, or computer science who want to apply data science to healthcare. It suits recent graduates, working professionals, and researchers passionate about using data to solve healthcare challenges and improve patient outcomes.

The MS in Health Data Science cohort includes students from diverse fields, such as mathematics, computer science, healthcare, engineering, and public health. Many have professional experience in healthcare, data analytics, or tech, while others are transitioning into the field.

• On-campus cohort size: 30–40 students.

This is a full-time program, requiring approximately 55 hours per week of coursework and class time.

While it is possible to work while enrolled, the program’s full-time nature requires a significant time commitment. Some students manage part-time work with flexible or remote hours, but many focus on research assistantships or internships that align with their academic goals.