Hi Jon,

The data carpentry ecology lessons do try to use the same (or very similar) 
data. So doing OpenRefine -> SQL -> SQL from Python, works very well. In my 
opinion, when you are able to run these lessons one after the other, it makes 
the workshop more coherent for the learners, but the lessons are still complete 
and can stand on their own.


Cheers,

Carlos


________________________________
From: Discuss <[email protected]> on behalf of Jon 
Pipitone <[email protected]>
Sent: 20 August 2016 16:56:43
To: [email protected]
Subject: [Discuss] Teaching lessons around a single example dataset/scenario

Hi all,

A few of us at the Centre for Addiction and Mental Health in Toronto
have been teaching a series of Software Carpentry-like workshops[1]
(actually some of them are exactly SWC workshops) over two weeks aimed
more specifically at researchers in our organization. Much of what we
teach is very introductory: what is programming, how to use the linux
shell, a very basic intro to R focusing on statistics, a MATLAB and SPSS
primer, using Photoshop, etc.. We do also get to a few more advanced
topics in some workshops: e.g. doing fMRI analysis in python, using a
compute cluster.

We received feedback from learners and instructors that having more
cohesion between the lessons would be really helpful to tie things
together (currently we have a mixture of lessons with toy examples, more
elaborate worked examples, and some with only descriptions/powerpoint),
but it's disjointed: there isn't a theme or example dataset running
through the workshops.

Has anyone tried creating lessons for several different topics around a
single example scenario? E.g. using Nelle's data from the Shell
lectures[2] to also teach R, Python, Git, etc.. How has it worked out?
Is there anything we should be wary of as we wander down this road?

Thanks!
Jon.

[1] e.g. https://camh-scwg.github.io/compucool
[2] http://swcarpentry.github.io/shell-novice/01-intro/
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