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BIOMEDIN 215: Data Science for Medicine (Aut 2021)

We will decide by early September whether the Autumn 2021 class will be offered in person or stay online. To preserve the learning experience of the pre-COVID in a virtual setting, enrollment in 2020 was limited to graduate students in the Biomedical Informatics (BMI) Program. Having made the transition, we look forward to welcoming more students this year.

– 2021 Teaching team: Nigam Shah, Alison Callahan, Eric Sun, Juan Manuel Chaves, Katelyn Bechler

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 2021 is here (log in to GSuite with your sunet id)
Canvas site is availabe here
Schedule: TUE, THU 1:30 PM - 2:50 PM
Prerequisites: CS 106A or equivalent, STATS 60 or equivalent
Recommended: STATS 216, CS 145, STATS 305


biomedin215.txt · Last modified: 2021/09/15 16:06 by nigam