Closed SaraMati closed 5 years ago
a few notes:
Amazing! I think any efforts for getting computational training going are needed!
Thoughts:
If you decide to focus more on the project management/workflow setup, overall code organization and reproducibility side of things, then we could merge some of the things I plan on developing for the "reproducibility and open science toolkit" set of projects.
@SaraMati did you see the discuss list posting about https://bioinformatics.ca?
@QuLogic I haven't seen any discussions regarding that workshop. where do you refer to?
@lwjohnst86 true, the course may be fit for a one year course. this first draft was more for you (UofT council folks) to see if you are up to develop another course based on the r-course.
ans yes, we should teach the languages. This is meant to differentiate the course with the computer science programming courses. I think the approach in teaching programming languages through scientific applications in the carpentaries and the r-course is good.
So, I don't quite understand your comment on separating the coding/programming from modelling/data handling? Isn't the r-course a combination? isn't the approach to teach programming for science, and hence in the lectures we have to talk about some models! I agree that then in the project part we should leave it to students and what is relevant to the projects. [a review of the r-course project can be help].
In general, I agree that the content of the course should be better determined. there are factors that should be determined from the institute side that affects the content:
Thanks for your input, I assume, the content will form through such discussions between people who sign up for developing the course.
@SaraMati yes, you are right about the rcourse and the coding vs modelling. I more meant to make sure it is solidly in the area of applying those models in a practical and hands on environment, rather than in the theory and details of the modelling/statistics.
Looking forward to hearing what they say about the idea. As I said, I can contribute material that overlays with what I plan on doing anyway :)
to follow up the idea that I raised with you a while back on designing a graduate course. Please pour in your ideas and whether you want to be part of it. This is a very rough draft:
Biomedical data science
Target population
Graduate students that are from different backgrounds and have entered the Neuromodulation/Neuroscience/biomedical engineering program
Course Description
Biomedical data includes a diverse range of data types from patient data from national registeries, to biomarker data, to genetic and sequence data, to electrophysiological recordings from human or animal models, to simulations; usually covering multiple spatio-temporal scales. These data are usually high dimensional, noisy and fragmented. Handling such data is challenging and understanding it is the very important first step in a data-driven approach to build predictive models. This course introduces techniques to analyze biomedical data using programing languages Python and R. The focus of the course is to integrate reproducible research principles such as well-documented modular coding, correctness of procedure, and interpretability of results and presentation of outcomes of the analysis.
Learning Outcomes
Ideas for course syllabus
Programming
Evaluation