Programming for Health Data Science in R (DATASCI 213)

Programming for Health Data Science in R
(DATASCI 213)

Summer 2026 (2 units)

Vast amounts of health-related data are being generated daily and at an increasing rate. Our ability to extract insights and make the most of these resources depends on the effective and efficient use of computational tools to preprocess, visualize, and analyze different types of data. BIOSTAT 213 is a programming course which aims to provide hands-on experience in the R language and enable further work in biostatistics, epidemiology, and machine learning/health data science.

Online Syllabus

Objectives

At the conclusion of this course, students will be able to:

  • Employ core R data types and structures.
  • Perform data input/output operations.
  • Perform filter, select, and group-by operations.
  • Implement control flow.
  • Summarize data.
  • Generate data visualizations.

The aim of the course is to provide a solid understanding of the core R language which allows students to then make use of the extensive collection of R packages available.

Prerequisites

No prior programming experience is required.

Faculty
Format

The class meets once a week on Wednesdays from 10:15 AM to 12:30 PM, July 15 - August 26, 2026. Using an interactive format, the initial portion of the session reviews 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 the following Monday.

All course materials and handouts will be posted on the course's online syllabus.

Students join the class Discord server, where they can interact with each other, the TAs, and the instructor.

Materials

Programming for Data Science in R by E. Gennatas (2020)
R version (latest version)
RStudio version (free Open Source License version, lastest version), or
VS Code with vscode-R extension
 

Grading

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 Quarto.

Official UCSF transcripts are not available for individual courses taken within the Department of Epidemiology and Biostatistics.  Students not in full-year TICR 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.

UCSF Graduate Division Policy on Disabilities

To Enroll