This neuroimaging data science course aims to engage students with coding in Python (and R) and best practices for reproducible science, focusing on applying these techniques to neuroimaging research. This course is ideal for any students with an interest in functional magnetic resonance imaging (fMR), structural MRI, diffusion-weighted imaging (DWI), and electroencephalography (EEG).

This course will build on the existing "BrainHack School," a program started as a graduate course at the Université de Montréal (PSY 6893) and now offered in six institutions globally (i.e., Philipps-University Marburg, Germany; Vanderbilt University, Nashville, U.S.) including a joint offering in Toronto at the University of Toronto and Toronto Metropolitan University in collaboration with the Centre for Addiction and Mental Health (CAMH), SciNet, Ontario Brain Institute, and SickKids.

All course offerings are linked via shared resources, a shared discord server for global project support, and shared events (i.e., "opening and closing ceremonies" and guest lectures/panel presented in hybrid format). In Toronto, the course functions using a "travelling classroom" model, allowing students to be exposed to multiple research perspectives across the city. With each classroom setting, a short guest lecture will welcome the students to the location and introduce them to the research being conducted at their site.

Additional learning opportunities through BrainHack School are available to students, however, this 0.25 FCE modular course will focus on project definition, implementation, and results analysis. The course director, who is a member of the BrainHack School faculty, will oversee the completion of course requirements to ensure they comply with the expectations of the graduate unit. The IMS students will be interacting with the external students within the BrainHack program. The IMS students will be considered its own cohort, and will receive instruction from the course director.