Master's Degree in Health Data Science

Learn more about the HDS Program by viewing the latest MS & Certificate Info Session

Data science plays a fundamental role in health sciences research: Learning from data is at the core of how we make advances in health research. Data science methods and tools are needed to deal with the expanding role of precision medicine, the widespread analyses of electronic health records, and the growing number of large and complex datasets.

The UCSF Master of Science (MS) Degree in Health Data Science (MiHDaS) is a two-year, in-person program in which students learn to apply biostatistics, data science and epidemiological thinking in clinical research settings.

The program is intended for:

  • Quantitative science learners interested in studying data science with a focus on biomedical applications.
  • Numerically able biomedical scientists interested in applying data science methods in clinical, epidemiological and biological sciences.

We also offer a one-year certificate program (CiHDaS), with condensed coursework and absent teaching and hands-on capstone project experience, best suited for those already working in the biomedical or pharmaceutical industries.

Curriculum

Master’s degree students are required to complete a minimum of 35.5 units of coursework and a capstone project over a 2-year period. Students take the majority of their coursework in the first year, allowing for focus on independent research in the second year. The required courses are listed in the table below. Students are welcome to take additional elective courses within the Department of Epidemiology and Biostatistics (DEB). Please note, as a self-supporting program, the University does not allow Health Data Science students to take additional courses outside of DEB. Elective courses may be drawn from this list of DEB classes. 

Courses

Credits

Year 1

 

Summer Quarter

 

Responsible Conduct of Research (EPI 201)

0.5

Opportunities and Challenges of Complex Biomedical Data (BIOSTAT 202)

3

Introduction to Programming for Health Data Science in R (BIOSTAT 213)

2

Fall Quarter

 

Epidemiologic Methods I (EPI 203)

4

Programming for Health Data Science in R II (BIOSTAT 214)

3

Biostatistical Methods for Clinical Research I (BIOSTAT 200)

3

Data Science Program Seminar I (DATASCI 220)

1

Winter Quarter

 

Biostatistical Methods for Clinical Research II (BIOSTAT 208)

3

Machine Learning in R for the Biomedical Sciences (BIOSTAT 216) 

3

Applied Data Science with Python 2

Data Science Program Seminar I (DATASCI 220)

1

Spring Quarter

 

Biostatistical Methods for Clinical Research III (BIOSTAT 209)

3

Understanding Machine Learning: From Theory to Application (DATASCI 224)

3

Data Science Program Seminar I (DATASCI 220)

1

   

Year 2

Fall Quarter

 

Advanced Machine Learning for the Biomedical Sciences II (DATASCI 225)

3

Data Science Program Seminar II (DATASCI 221) 1

Data Science Capstone Project

8

Winter Quarter

 

Data Science Program Seminar II (DATASCI 221)

1

Data Science Capstone Project

8

Spring Quarter

 

Data Science Program Seminar II (DATASCI 221)

1

Data Science Capstone Project

8

Educational Practice (DATASCI 300) *

1

TOTAL CREDITS

63.5

Note: Coursework is minimal in year 2 of the Master's program to allow students to complete capstone requirements: first-authored publication submission, conference presentation, background/methods report and personal portfolio.

 

* Education Practice may occur in any quarter of year 2.

Capstone Project

Students will begin developing a longitudinal capstone project as part of their requirements for the MiHDaS degree. Identification of the project will be encouraged in the first part of the program with the help of their UCSF faculty mentors (i.e. the members of their Graduate Committee), one of whom will be the Graduate Committee Chair, one the data science/biostatistics/bioinformatics faculty and one a clinical faculty member within UCSF.

The required capstone project encompasses four components:

  1. Submission of a first authored publication in a scientific journal that is data science, general science or medical applications-based (this does not need to be accepted, but does need to be approved by the student’s Graduate Committee);
  2. Giving an oral or poster presentation at a scientific conference;
  3. Writing a report on the background methodology and technical issues that were adopted or considered for the submitted publication. This report is expected to provide more detail to demonstrate solid understanding by the student of the technical methods used including full literature review with respect to the history of methods development, and
  4. Compiling a code and analysis portfolio for marketing the student’s career skills.

These components were chosen to emphasize the crucial skills necessary to be a successful data scientist that go above and beyond purely technical skills. This includes but is not limited to:

  • carefully describing methodology used in a written format,
  • presenting work orally, and
  • conveying the importance of one’s work in peer-reviewed publications and elsewhere.

This capstone element effectively provides students with an “apprenticeship” of sorts in the field of Data Science for the Health Sciences. By producing a submitted scientific paper approved by their committee, giving a presentation, and writing a methodological report, MiHDaS graduates will be able to clearly demonstrate that they are qualified work in the field as part of a Health Sciences team.

Educational Practice

Students in the program will be expected to gain instructional experience for one course during their second year. This experience typically involves leading a weekly small-group discussion section of 10 to 15 students, holding office hours for students and grading homework assignments and projects. This requirement is designed to provide students with a valuable instructional experience without having a significant impact on the time needed for their Capstone project work. In all cases, students will have taken the courses during their first year.

This instructional experience provides students with important skills while working under the guidance of experienced faculty that they can subsequently transfer into the workplace. Even if they are not working in academia, the ability to explain concepts and interpret results for other members of the team are critical skills for a data scientist that they will acquire in their instructional experience.

Health Data Science Bylaws

UCSF Graduate Program Health Data Science (HDaS) Bylaws_APPROVED 11172022

Admissions

The application for entrance in Summer 2024 is now open. Application deadlines are detailed below in the “How To Apply.” Apply Now!

Minimum requirements for admission are:

  • Bachelor’s degree (BA/BS) or the equivalent from an accredited institution in a quantitative or biomedical science, or related field, with a minimum grade point average of 3.0.
  • International applicants from non-English speaking countries must also demonstrate proficiency in English by:
    • Completing one year of full-time study with a minimum GPA of 3.2 at a college or university in the United States that has been accredited by an accreditation agency or state agency recognized by the U.S. Department of Education, or
    • Earning a degree from a college or university outside of the United States with instruction fully in English, or
    • Obtaining the minimum scores on the Test of English as a Foreign Language (TOEFL) - administered by ETS, or the International English Language Testing System (IELTS). Please see the Graduate Division’s International Admission Requirements for minimum scores; note that the Health Data Science program minimum internet based TOEFL iBT score is 100. Test scores are valid from these institutions for a maximum of two years from the test date. TOEFL official scores must be sent to UCSF’s institutional code 4840; for IELTS scores, email a copy of your score report to [email protected].​​​​​​

International students who have completed degrees in countries where English is the native language are exempt from the testing requirement.

How to Apply

  • Timeline:
    September 15, 2023: Application opens for the 2024-25 year
    January 15, 2024: International student priority deadline (applications received by this date will be reviewed first)
    April 1, 2024: Final application deadline
    Mid-July 2024: Admitted students begin courses
    Applications are reviewed on a monthly basis. Early application submission is particularly encouraged for applicants originating outside of the San Francisco Bay Area who may require housing or student visas and for applicants who will need to apply for financial aid. Submit the following required materials to the UCSF Graduate Division online application.
  • Transcripts. Applicants must upload unofficial transcripts from every academic institution attended, even if you did not receive a degree. Alternatively, you may upload a scanned copy of an official transcript. The program does not require official transcripts until admission. If you completed coursework/degrees outside of the United States, you must submit transcripts translated into English. You may upload an unofficial copy of your World Education Services (WES) course-by-course credential evaluation; however, this is optional for your application and you do not need to send your official WES report until you are admitted. Additionally, if your university does not issue grades in a 4.0 grade point scale, you will need to convert your grades for the application. The World Education Services provides a free online GPA calculator for this purpose.
  • Three Letters of Recommendation. These letters should be from individuals who are familiar with the nature of your preparation for graduate school and who can provide insight into your potential to succeed as a scientist. We require that at least one letter be from someone who is familiar with your academic strengths and weaknesses, ideally a faculty member with whom you have worked during your education.
  • Resume or Curriculum Vitae. Include all applicable information including but not limited to: education; honors and awards; positions held; membership and service in societies; research experience/projects; computer skills; language proficiency; extracurricular interests; and community service.
  • Statement of Purpose (2,000 character limit including spaces). Describe your reasons for interest in the program. Include your objectives, potential research interests and goals, and how you envision acceptance into the program can help you accomplish these.
  • Personal History Statement (2,000 character limit including spaces). You are welcome to share any other information that is not included in your CV or elsewhere in your application that might be relevant for the admissions committee to know, such as: What unique characteristics you bring and can contribute to a cohort of master’s students. If you have below a 3.0 GPA or if there is an area of potential concern in your transcript or CV, please address any extenuating circumstances.
  • Application fee: U.S. citizens or permanent residents may qualify for an application fee waiver. International applicants are not eligible to apply for fee waivers. See guidelines on Application Fee Waivers to determine your eligibility for this exemption. To request the waiver, select the “application fee waiver” option in the payment area of the online application.
  • TOEFL or IELTS scores (see above minimum requirements for international applicants from non-English speaking countries)