Individual Courses

Online and in-person courses in Training in Clinical Research Program (TICR), online courses in implementation science, as well as a mini-course in implementation science are available to individuals within the UCSF community and from other institutions.

Summer Quarter Courses
Fall Quarter Courses
Winter Quarter Courses
Spring Quarter Courses

2023-2024 course fees

Contact [email protected] for questions about enrollment or follow the current quarter enrollment link on this page.

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.



Publishing and Presenting Clinical Research (EPI 212)
Instruction in preparing abstracts for scientific conferences; the essential components of a research manuscript (Introduction, Methods, Results, Discussion); and navigating the submission and revision aspects of the peer-review process.

Systematic Reviews (EPI 214)
Instruction in the methods of systematic and unbiased identification of primary research studies; abstraction of data; determination of summary estimates and evaluation of heterogeneity.

TICR Program Seminar for First-Year Master’s and Certificate Program Scholars  (EPI 220/230)
Biweekly seminar for first-year students to present their research to their colleagues and faculty.

Masters Seminar II Spring (EPI 221)
Biweekly seminar for second-year students to present their projects and specialized methodologic topics to their colleagues and faculty. 

Use of Electronic Health Record Data for Research (EPI 231)
Introduction to electronic health record (EHR) data, including relational database and data warehouse models as they pertain to EHRs; medical vocabularies and ontologies used in EHRs; construction of patient cohorts based on structured data, such as diagnosis codes, encounters, and procedures; extraction of relevant associated data for a specified patient cohort into analytic files; and formulating research questions that benefit from the strengths and limit weaknesses of EHR data.

Study Designs for Intervention Research in Real-World Settings (EPI 241)*
Instruction in the design of studies that are alternatives to individual participant-level randomization for the evaluation of interventions in real-world settings. Both randomized (e.g., cluster-randomized and stepped-wedge randomized trials) and quasi-experimental design (e.g., pre-post and interrupted time series) will be discussed.

Designing Interventions to Change Organizational Behavior (EPI 247)*
Instruction in translational tools at the health care system level to promote the adoption of evidence-based medicine by the public and providers through mechanisms that influence health care delivery systems.

Translating Evidence Into Policy: Framing Research to Influence Policy (EPI 249)*
Instruction in types of questions that can be addressed with large administrative and clinical databases; gaining access to these databases; determining validity of information; risk adjustment; linking datasets; and building registries. Instruction in the policy process and strategies for collecting and disseminating research findings to inform and influence that process. The course will be taught through a series of lectures and interactive sessions during which trainees will have an opportunity to apply the strategies to their own work.

NIH F & K Grant Writing Workshop (EPI 258 B)
Will not be taught during 2023-2024 academic year.
This course is designed to clarify early investigators’ research and career goals and to learn the different components of NIH pre- and post-doctoral grants.

Epidemiologic Methods III (EPI 265)
Will not be taught during 2023-2024 academic year.
This course will focus on clearly articulating and testing research hypotheses related to the determinants and consequences of chronic conditions. Each session will introduce specific methodological concepts for epidemiologic studies, organized around an illustrative applied research paper. The course will emphasize causal inference from observational data. Most examples will be drawn from literature on social and lifecourse determinants of dementia, stroke, and cardiometabolic disease.

Mathematical Modeling of Infectious Diseases (EPI 266)
Will not be taught during 2023-2024 academic year.
Introduction to concepts of mathematical modeling of infectious diseases; topics include branching processes and the basic reproduction number, dynamical systems, and methods for data fitting including Markov chain Monte Carlo.

Econometric Methods for Causal Inference (EPI 268)
Will not be taught during 2023-2024 academic year.
Instruction on estimating the causal effects of health-related policies, programs, and interventions using observational data and methods developed in the field of econometrics. Topics include difference-in-differences, instrumental variables, and regression discontinuity.

Biostatistical Methods for Clinical Research III (BIOSTAT 209)
A continuation of the Winter Quarter course in multivariable statistical analysis that includes instruction in survival analysis and analysis of repeated measures and clustered data. The course culminates with student presentations of statistical analyses of their own research projects.

Advanced Approaches to the Analysis of Observational Data (BIOSTAT 215)
A common goal of observational clinical or epidemiologic research is to estimate the causal effect of particular exposures or interventions on some health outcome. This course will describe more advanced methods that may succeed in estimating causal effects in cases where standard approaches break down..

Understanding Machine Learning: From Theory to Applications (DATASCI 224)
This course teaches the mathematical foundations of machine learning (ML). Each week, the course surveys a different algorithm to examine its underlying machinery, covering topics such as linear algebra, calculus, and optimization. ML algorithms range from linear models to gradient boosting and deep learning. The course also discusses newer concepts such as model fairness and ML for causal inference. Upon course completion, students should be able to learn new ML algorithms independently.

Advanced Machine Learning for the Biomedical Sciences II (DATASCI 225)
Will not be taught during 2023-2024 academic year.
Extends upon BIOSTAT 216 (introduction to machine learning) to provide instruction in a deeper mathematical and statistical understanding of machine learning algorithms to solve problems of prediction, pattern recognition and data reduction. Students will learn how to manipulate and customize popular machine-learning algorithms to best satisfy specific research needs. The R software environment will be used throughout.

Grant Writing Workshop on Mentored Career Development Awards
Instruction in writing successful grant applications for NIH-mentored career development awards. The workshop uses examples from patient-oriented research career development awards (K23s). Underlying concepts for the career development plan, mentoring plan, and research plan also apply to research scientist development awards (K01s) and clinical scientist development awards (K08s). 

*enrollment in this course (or track) not guaranteed due to high demand