Below we provide answers to frequently asked questions. More information about program requirements and coursework can be found in the HDS student handbook.
I haven’t taken the required math courses. Do online courses fulfill this requirement? Do I need to complete them before the application deadline?
Yes, online courses through platforms including Coursera, edX, DataCamp, Udemy, etc. fulfill the math course requirement. Be sure to include which courses you either completed or plan to complete in your resume and/or statement of purpose. Online courses do not need to be completed by the application deadline, but do need to be completed before the program begins, if you are accepted into the program.
Is work experience required?
No. While work experience may be beneficial for the successful completion of program coursework, it is not required for program eligibility.
Is there an internship component?
No. Students are able to apply for summer internships while enrolled in the program, but internships or work experiences are not guaranteed or part of the program curriculum. However, we do provide an abundance of career services to help guide students through the application and interview process and find internship opportunities.
How many letters of recommendation are required?
A minimum of 3 letters are required, but a maximum of 5 letters may be submitted.
Is there an interview component to the application?
Do you require a WES evaluation of GPA for international students?
Is it possible to finish the program in 2 semesters?
No. The program has been specifically designed to take place over 3 semesters. Due to the amount of credits and requirements for the degree, finishing in 2 semesters is not possible.
Am I able to apply before I have completed any of the eligibility requirements (e.g. undergraduate degree, coursework, etc.).
Yes. As long as you indicate on your application that the requirements will be fulfilled before the start of the program, you are able to apply.
Do my GRE scores need to submitted before the December 1st deadline?
The unofficial scores must be submitted by December 1st, but your official GRE scores may be submitted by January 15th. If you have any questions or concerns about meeting this deadline, please contact the Admissions Office at 617-432-1031 or email@example.com.
Is the program more research or career-oriented?
The program is more career-oriented with the degree intended to be a terminal degree that prepares students for a career in a related field. However, the skill set learned is also beneficial for a research-oriented career or future degree.
Do you need a background in Biomedicine or Public Health?
No. No prior public health or biology knowledge is required for admission. The curriculum is predominantly focused on statistics and computing with health science applications. Students can take a wide range of courses in biomedicine and public health to gain substantive science knowledge and are required to take an introductory Epidemiology course for exposure to public health practices and concepts.
Are courses more focused on Biology because of the ‘health’ in Health Data Science?
No. Most courses focus on statistical and computational methods that are commonly used in the analysis of biomedical data, but the courses do not require prior knowledge of biology.
What are the average or expected admission scores? (GRE, TOEFL, GPA, etc.)
In general, the expectation is for the student to have excelled in past studies and to have demonstrated strong ability in a quantitative field with a strong interest in health data science. There are no minimum test scores, but competitive scores are highly recommended.
How can I better prepare myself for the application?
Multivariable calculus, linear (matrix) algebra and proficiency in a computer programming language, such as R or Python, are required for eligibility of admission. The best way to prepare yourself is to take mathematics, statistics and computing courses at academic institutions. You may also complement your skill set by taking additional courses online, such as with EdX and Coursera. See the resources page for a list of helpful courses.
What constitutes as adequate statistical training?
Multivariate calculus and at least one semester of linear or matrix algebra is required for admission eligibility. Courses in probability and statistical inference are highly recommended, but not required for admission.
Is academic or industry training better preparation for the application and program?
Industry training is not a requirement for admission but does show interest and experience in the field. Industry training is also not a requirement for the program but may be helpful during the program. Academic training is a requirement of the application and will be beneficial before starting the program. See the Resources tab for a list of topics and online courses we recommend to review before starting the program.
How is the Health Data Science program different from other data science programs?
Compared to other data science programs, we provide interdisciplinary training in statistics, computing and health science. Specifically, our program has a strong focus on integrating statistical inference and big data computing with solving important problems in public health and biomedical sciences. We emphasize the importance of statistical inference and scalable computational tools, as well as gaining knowledge of health science in our curriculum. Students are trained to become interdisciplinary quantitative leaders in analyzing and interpreting massive and complex data in health sciences.
What does ‘health data’ mean, and why is it important?
Health data refers to any data pertaining to the biomedical sciences and public health. Data sets might originate from observational studies, clinical trials, computational biology, exposome, electronic medical records, health care claims, genetic and genomic epidemiology and environmental health, digital phenotyping, network health science, and many other fields.
Can I take courses at other Harvard schools and/or MIT?
Yes. Students can cross-register for courses at all Harvard schools and MIT. However, only graduate-level courses may be taken.
What is your capstone course?
The 7.5 credit Health Data Project Course is a semester-long, project-based research course that will allow students to gain practical skills in analyzing and interpreting different types of big data in public health and biomedical science. Students are grouped into teams of 2. Each team will be assigned a Harvard affiliate mentor who will grant access to data at the mentor’s respective institution and assign a semester-long project. Each team will be expected to work on the project outside of class and attend lectures focusing on project planning and execution, career development and special advanced topics in data science.
Can a thesis be done for the capstone course?
No. Students are assigned specific individual and group projects for the capstone course. While there is a writing component, the course cannot be made into a thesis.
What resources are available to me as a student?
There are a multitude of resources available to students, such as networking, career services and research opportunities with faculty and other researchers.
Can I enroll as a part-time student?
That decision is made by the Harvard T.H. Chan School of Public Health office of the registrar. Admitted students must first petition to enroll part-time, and then receive approval in order to do so.
Could I start the program in a Spring semester?
No. Each incoming cohort begins the program in the Fall semester.
What opportunities await me after graduation?
Possible career trajectories include working as a data scientist, data analyst, machine learning engineer, statistician, software engineer, and quantitative analyst in academia, government or industry to work on biomedical and public related areas. It is also possible for students to further their education in a doctoral program in a related field.
If I pursue a career outside of public health after degree completion, is the skill set transferable?
Yes. While many of the examples and problems in the course curriculum will center around current topics in public health and biomedicine, the statistical and computational skill set and tools are broadly applicable to many areas of data science.