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Degree Offered: MS
Program Leadership:
John Kornak, PhD, Program Director
Admissions Inquiries:
Eva Wong-Moy, Graduate Affairs Manager
Program Description
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 Master of Science (MS) Degree in Health Data Science (MiHDaS) is a two-year program in which students learn to apply data science, biostatistics, machine learning, 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.
Admission Requirements
- 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 (B average) on a 4.0 scale at an accredited college or university in the United States, 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, select University of California, San Francisco - Graduate Division.
- International students who have completed degrees in countries where English is the native language are exempt from the testing requirement.
- Three letters of recommendation
- Resume or curriculum vitae
- Statement of Purpose
- Personal History Statement
Learning Outcomes
To complete the program, scholars must satisfy program objectives, which are to:
- Acquire a mastery of a broad set of data science research methods and in the techniques needed for the application of data science across biomedicine applications and research.
- Gain understanding of key issues that are particularly pertinent to the health sciences and evidence-based medicine, such as bias, confounding, interpretability, and causality.
- Plan and implement one or more health-related data science research projects.
- Write a publication-quality first-author research paper and a detailed methodology review.
- Present research results at a national or international meeting.
- Create a portfolio of data science skills and application areas.
Degree Requirements
- All core courses and required activities taken and passed with a grade C or higher.
- Maintain a cumulative GPA of 3.0 or higher (equivalent to a B average).
- Capstone project
- Educational apprenticeship (teaching assistant for one course)
- Unit requirement: 36 units
Core Courses
Plan of Study Grid
Year 1 |
Summer |
BIOSTAT 202 |
Opportunities and challenges of complex biomedical data |
3 |
BIOSTAT 213 |
Programming for Health Data Science in R |
2 |
EPIDEMIOL 201 |
Responsible Conduct of Research |
0.5 |
| Units | 5.5 |
Fall |
BIOSTAT 200 |
Biostatistical Methods in Clinical Research I |
3 |
BIOSTAT 214 |
Programming for Health Data Science in R II |
3 |
EPIDEMIOL 203 |
Epidemiologic Methods |
4 |
DATASCI 220 |
Data Science Program Seminar I |
1 |
| Units | 11 |
Winter |
BIOSTAT 208 |
Biostatistical Methods II |
3 |
BIOSTAT 216 |
Machine Learning in R for the Biomedical Sciences |
3 |
DATASCI 220 |
Data Science Program Seminar I |
1 |
DATASCI 223 |
Applied Data Science with Python |
2 |
| Units | 9 |
Spring |
BIOSTAT 209 |
Biostatistical Methods III |
3 |
DATASCI 220 |
Data Science Program Seminar I |
1 |
DATASCI 224 |
Understanding Machine Learning: From Theory to Applications |
3 |
2 |
2 |
| Units | 9 |
Year 2 |
Fall |
DATASCI 221 |
Data Science Program Seminar II |
1 |
DATASCI 222 |
Data Science Capstone Project |
8 |
DATASCI 300 |
Data Science Educational Practice 1 |
2 |
| Units | 11 |
Winter |
DATASCI 221 |
Data Science Program Seminar II |
1 |
DATASCI 222 |
Data Science Capstone Project |
8 |
| Units | 9 |
Spring |
DATASCI 221 |
Data Science Program Seminar II |
1 |
DATASCI 222 |
Data Science Capstone Project |
8 |
| Units | 9 |
| Total Units | 63.5 |
Additional Information
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 from the data science/biostatistics/bioinformatics faculty and one a clinical faculty member within UCSF.
The required capstone project encompasses four components:
- 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);
- Giving an oral or poster presentation at a scientific conference;
- 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
- 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 to work in the field as part of a Health Sciences team.
Educational Apprenticeship
Students in the program will be expected to act as an educational apprentice (EA) 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 teaching experience without having a significant impact on the time needed for their capstone project work. In all cases, students will have taken during their first year the courses that they are asked to EA. Students will enroll in DATASCI 300 Data Science Educational Practice to receive credit in the term they EA.
Acting as EAs 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 role as EA.