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--- biomedin215 [2020/08/26 15:30]
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+++ biomedin215 [2024/07/21 15:27] (current)
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-====== BIOMEDIN 215, 2020 ======
+====== BIOMEDIN 215: Data Science for Medicine (Aut 2024) ======
-In the Autumn quarter of 2020, this class will be offered online.
+The Autumn 2024 class will be offered in person.
-In the past years, the [[:biomedin215_precovid|pre-COVID]] offering was a highly interactive, in person experience. We wish to preserve as much of the learning experience as possible in a virtual setting. Therefore, to ease the transition of converting the class content for offering on Zoom, enrollment in 2020 will be limited to graduate students in the Biomedical Informatics (BMI) Program. I have chosen to do this so that we can maintain an effective learning experience for those students for whom this is a required class.
+– 2024 Teaching team: Nigam Shah, Yixing Jiang, Suhana Bedi, Sohaib Hassan, Joshua Lazaro
-If you are not a BMI graduate student, thank you for understanding. I look forward to meeting you in the regular offering of the class next Autumn.
+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 R or Python.
+Schedule: TUE, THU 3:00 PM - 4:20 PM in Gates B3. \\
+See the [[https://canvas.stanford.edu/courses/197530|Canvas site]] (log in with your SUNet id), for office hours, contact information, Syllabus, Prerequisites etc.
+Upon completing this course, you should be able to:
+  - recognize categories of research questions and the study designs used to address them.
+  - describe common healthcare data sources and their relative advantages and limitations.
+  - extract and transform various kinds of clinical data to create analysis-ready datasets.
+  - design and execute an analysis of a clinical dataset to answer a research question.
+  - apply your knowledge to evaluate and criticize published clinical informatics research.
+<html>
+<iframe width='900' height='580' frameborder='0's src="https://docs.google.com/spreadsheets/d/e/2PACX-1vQKjg1NifRWhIeIWoGfgXp7w8wJOGGL1Hvf556PayWog1RQs8PaYZDKtx65KcT0EaYOwlC33-0bLWTf/pubhtml?gid=34616104&single=true&widget=true&headers=false"></iframe></html> //

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