CSCI 6410
Applied Res. Health Data Sci
This course is an introduction to the application of data science methods to health data within interdisciplinary research contexts. Students will be introduced to the main types of health data and their principal analysis methods while developing key research skills specific to effectively working at the intersection of medicine and computer science. This will encompass developing technical skills in the robust/reproducible analysis of data from medical databases, radiological imaging, electronic medical records, and physiological time-series data. Students will also gain specific training in developing interdisciplinary health data science research proposals including key considerations such as research ethics, data legislation, knowledge translation, and effective collaboration.
Sections
From the syllabus
From the Summer 2026 syllabus by Finlay Maguire. Open the PDF
Grading
Textbooks
- R for Data Science, Wickham and Grolemund (free)
- Hands-On Machine Learning for R, Boehmke and Greenwell (free)
- Text Mining with R, Julia Silge and David Robinson (free)
Course rules
- Late work
- Assignments lose 20% per day; work over five days late receives zero, though it may be evaluated for feedback.
- Submission
- Submit assignments through Brightspace with a link to the matching Git repository; missing links incur a 40% penalty.
- Practical 0
- Attendance is required, but Practical 0 is formative and ungraded.
- Collaboration
- Collaboration is not permitted on practical assignments.
- Absences
- Student declarations of absence are not accepted.
- Graduate pass
- Graduate students need at least B-; marks below 70% convert to an F.
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