The Autumn 2022 class will be offered in person.

– 2022 Teaching team: Nigam Shah, Alison Callahan, Bryan Bunning, Oana Enache, Min Woo Sun, Maggie Wang

This course is designed to prepare you to pose and answer meaningful clinical questions using routinely collected healthcare data. The practical skills you will learn in this class will be applicable to any task involving data manipulation and analysis. The course will use real, de-identified, large size patient datasets for home work projects associated with the course. To have the best learning experience, you will need to be proficient in the R language.

Upon completing this course, you should be able to:

1. recognize categories of research questions and the study designs used to address them.
2. describe common healthcare data sources and their relative advantages and limitations.
3. extract and transform various kinds of clinical data to create analysis-ready datasets.
4. design and execute an analysis of a clinical dataset to answer a research question.
5. apply your knowledge to evaluate and criticize published clinical informatics research.

The syllabus for 2022 is here (log in to GSuite with your SUNet id) and the Canvas site is available here
Schedule: TUE, THU 1:30 PM - 2:50 PM in Gates B1. For Office Hours, see the Canvas site
Pre-requisites: This course is designed for students experienced in basic programming, statistics, and biology. To ensure your success in this course, we expect that you know the R programming language and require that you have taken the following courses:

• CS 106A or equivalent
• STATS 60 or equivalent
• high school biology