CSCI 4148

Applied Res. Health Data Sci

Lec 01, Lab B01 and Tut T01 selected

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

Lecture
Lab
Tutorial

From the syllabus

From the Summer 2026 syllabus by Finlay Maguire. Open the PDF

Grading

7.5%Practical 1: Medical DatabasesDue June 1
7.5%Practical 2: Electronic Medical RecordsDue June 12
7.5%Practical 3: Radiological ImagingDue June 19
7.5%Practical 4: Physiological Time-SeriesDue June 26
20%Proposal presentationJune 22–26
20%Proposal write-upDue June 29
20%Tutorial paper presentationThroughout
10%ParticipationThroughout

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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