Master's Degree in Health Data Science

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 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. 

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)

1.5

Fall Quarter

 

Epidemiologic Methods I (EPI 203)

4

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

1.5

Biostatistical Methods for Clinical Research I (BIOSTAT 200)

3

Data Science Program Seminar

1

Winter Quarter

 

Biostatistical Methods for Clinical Research II (BIOSTAT 208)

3

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

3

Data Science Program Seminar

1

Spring Quarter

 

Biostatistical Methods for Clinical Research III (BIOSTAT 209)

3

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

3

Data Science Program Seminar

1

   

Year 2

Fall Quarter

 

Master’s Seminar Series I

1

MiHDaS Capstone Project I

1

Winter Quarter

 

Master's Seminar Series II

1

MiHDaS Capstone Project II

1

Spring Quarter

 

Master's Seminar Series III

1

MiHDaS Capstone Project III

1

Educational Practice*

1

TOTAL CREDITS

35.5

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 act as a teaching assistant (TA) 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 the courses during their first year that they are asked to TA.

Students acting as TAs provides them 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 TA.