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Only one application needs to be completed for all courses desired during the quarter.
Deadline:
09/04/2026
Format:
In-person
Term Offered:
Fall
Overview
Health-related data is generated daily at an increasing velocity, from a multitude of sources. Our ability to extract insights to advance basic biomedical science and clinical practice depends on our ability to effectively curate, transform, and analyze data as well as present and communicate findings. This course builds on students' core R language knowledge to cover skills in advanced data transformations, visualization, working with big (in-memory) data, automated and reproducible report-writing, and core statistical procedures.
Course Details
At the conclusion of this course, students will be able to:
- Import, query, clean, transform, and analyze large datasets in R (using base R and data.table).
- Write custom data processing pipelines to address their individual analytical needs.
- Perform common statistical procedures.
- Apply skills developed in class to complete a project using their own data/data of their choosing.
Programming for Health Data Science in R (DATASCI 213). Exceptions to this prerequisite may be made with the consent of the Course Director, space permitting. Please review the DATASCI 213 syllabus to ensure that you are fully conversant with all of DATASCI 213 materials before requesting an exception on that basis.
Weekly lectures with demonstration and hands-on exercises. Using an interactive format, sessions begin with a review of the prior week's material and exercises. New material is introduced by reviewing code and with live demonstration in R. Participation is key to maximize learning for all students. Sessions end with labs where students have the opportunity to work on their weekly assignments, which are due two days before the following class at 5pm.
Students join the class Discord server, where they can interact with each other, the TAs, and the instructor.
- Programming for Data Science in R by E.D. Gennatas (2022): https://class.lambdamd.org/pdsr/
- R version 4.2.1 or higher
- RStudio Desktop version 2022.02.3-492 or higher (free Open Source License version); or
- VS Code with vscode-R extension.
Prior to class, please review chapters 1-14 and 38-39 of PDSR (https://class.lambdamd.org/pdsr/)
- Introduction: Ch. 1-5
- Data Types & Data Structures: Ch. 6-7
- Indexing: Ch. 8
- Factors & Data I/O: Ch. 9-10
- Vectorization & control flow: Ch. 11-12
- Summarizing & aggregating data: Ch. 13-14
- Visualization I: Ch. 38-39
Final grades will be based on the weekly assignments (60%) and the final project (40%). The final project will be in the form of a brief article on your choice of a dataset to be written in Rmarkdown.
Students not in full-year DEB Education Programs who satisfactorily pass all course requirements will, upon request, receive a Certificate of Course Completion.
Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.
For UC-Affiliated learners, the course fee for the 2-unit course is $2,100; for the 3-unit course is $2,500
For Non-UC-Affiliated learners, the course fee for the 2-unit course is $3,200; for the 3-unit course is $3,800
UC-Affiliation: Concurrent enrollment in a University of California-sponsored residency or post-doctoral fellowship program that is recognized by the Office of Graduate Medical Education; or a registered student in one of the professional schools or graduate programs at the University of California (in a program other than the TICR program); or individuals who hold full-time salaried University of California faculty, academic or staff positions. Please note: Individuals will be asked to provide proof of UC status.
At the conclusion of this course, students will be able to:
- Import, query, clean, transform, and analyze large datasets in R (using base R and data.table).
- Write custom data processing pipelines to address their individual analytical needs.
- Perform common statistical procedures.
- Apply skills developed in class to complete a project using their own data/data of their choosing.
Programming for Health Data Science in R (DATASCI 213). Exceptions to this prerequisite may be made with the consent of the Course Director, space permitting. Please review the DATASCI 213 syllabus to ensure that you are fully conversant with all of DATASCI 213 materials before requesting an exception on that basis.
Weekly lectures with demonstration and hands-on exercises. Using an interactive format, sessions begin with a review of the prior week's material and exercises. New material is introduced by reviewing code and with live demonstration in R. Participation is key to maximize learning for all students. Sessions end with labs where students have the opportunity to work on their weekly assignments, which are due two days before the following class at 5pm.
Students join the class Discord server, where they can interact with each other, the TAs, and the instructor.
- Programming for Data Science in R by E.D. Gennatas (2022): https://class.lambdamd.org/pdsr/
- R version 4.2.1 or higher
- RStudio Desktop version 2022.02.3-492 or higher (free Open Source License version); or
- VS Code with vscode-R extension.
Prior to class, please review chapters 1-14 and 38-39 of PDSR (https://class.lambdamd.org/pdsr/)
- Introduction: Ch. 1-5
- Data Types & Data Structures: Ch. 6-7
- Indexing: Ch. 8
- Factors & Data I/O: Ch. 9-10
- Vectorization & control flow: Ch. 11-12
- Summarizing & aggregating data: Ch. 13-14
- Visualization I: Ch. 38-39
Final grades will be based on the weekly assignments (60%) and the final project (40%). The final project will be in the form of a brief article on your choice of a dataset to be written in Rmarkdown.
Students not in full-year DEB Education Programs who satisfactorily pass all course requirements will, upon request, receive a Certificate of Course Completion.
Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.
For UC-Affiliated learners, the course fee for the 2-unit course is $2,100; for the 3-unit course is $2,500
For Non-UC-Affiliated learners, the course fee for the 2-unit course is $3,200; for the 3-unit course is $3,800
UC-Affiliation: Concurrent enrollment in a University of California-sponsored residency or post-doctoral fellowship program that is recognized by the Office of Graduate Medical Education; or a registered student in one of the professional schools or graduate programs at the University of California (in a program other than the TICR program); or individuals who hold full-time salaried University of California faculty, academic or staff positions. Please note: Individuals will be asked to provide proof of UC status.
Meet the Faculty
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Stathis Gennatas, MBBS, PhD
Assistant Professor, Department of Epidemiology & Biostatistics
Email
How to Enroll
This course is restricted to those enrolled in the Advanced Training in Clinical Research Certificate Program (ATCR) and the Master's in Clinical Research Degree Program. A limited number of spaces are available to non-TICR students. Please complete the course application form to indicate interest in participation. Below you’ll find details on course fees, how to apply and pay, and the full course schedule. Be sure you understand the payment process before applying.
And remember: you only need to submit one application for all the courses you want to take per quarter.
Health Data Science/Clinical Research (ATCR/MS) and PhD students: Please use the Student Portal to add the course to your study list.
- Please review:
- Course Fees (2025-2026)
- How to pay (please read before applying)
Only one application needs to be completed for all courses desired during the quarter.
How to Enroll
Whether you're exploring the field, enhancing your skills, or working toward a larger academic goal, most individual courses are open to everyone. Current learners enrolled in DEB Education programs have priority in enrollment for this course. Any extra spaces are available to non-DEB Education learners (i.e., those from the Institute for Global Health Sciences, School of Nursing, external groups, etc).
You’ll find details on course fees, how to apply and pay, and the full course schedule on the Course Enrollment & Payment page. Be sure you understand the enrollment policies and payment process before completing the enrollment form.
And remember: you only need to submit one form for all the new courses you want to take.