Course planning

[rvosa]

What do we want to do:

• describe learning objectives
• describe target learner and pre-requisites
• identify learning outcomes and scope (Establish scope #2)
• decompose into subsections, e.g. re-runnability, data provenance, etc. (Establish scope #2)
• develop exercises, at least one for each learning outcome, but actually very many
• collect readings https://www.mendeley.com/community/osodos/ (Manage references #10)
• prepare lectures
• plan documentation

[rvosa]

Hi @Pfern,

on the issue of exercises, we are planning to work through a single case where a number of analysis steps are performed. For this it will be most instructive if this is done with one of the platforms that best supports literate programming, so that the analysis can be mixed with the prose of the example publication. As far as I know, there are two plausible platforms for this: jupyter or rmarkdown. Here are some places that aggregate example cases:

• jupyter: https://github.com/nborwankar/LearnDataScience (alert, examples 3 years old)
• jupyter: https://github.com/jakevdp/sklearn_tutorial (<1 yr old)
• rmarkdown: https://ropensci.org/usecases/

All the examples are relatively stats-heavy heavy but light on data volumes, for relatively obvious reasons, them being on github. This also means they are more geared towards ecology (e.g. niche modeling) than bioinformatics per se. Should we nonetheless pick one from among these?

[Pfern]

We could throw-in some simple problems that start from sequence data.
I will find some...

Also from time series like this one
https://github.com/simplystats/simplystats.github.io/blob/master/_Rmd_files/2016-05-05-timeseries-biomedical.Rmd

Also see, for the advocacy of our cause
https://www.nature.com/sdata/policies/repositories
http://blogs.plos.org/plos/2017/04/open-data-projects-win-wellcome-trust-nih-and-hhmi-open-science-prize/
https://phys.org/news/2017-02-science-prize-software-tool-tracking.html

[Pfern]

The ScikitLearn is impressive!