Hi Jason,
I've taught the inflammation course a few times to absolute beginners
and that always worked well, the very first lesson may be overwhelming
but people seem to have a nice 'aha' moment once they make that heatmap
- yesterday at UWA in Perth we taught Data Carpentry's Intro to Python
from the ecology materials, it's very much focused on pandas and
manipulation of data stored in dataframes:
http://www.datacarpentry.org/python-ecology-lesson/ The data is
ecological (species survey) but the lesson would work with any CSV.
I'd chose the inflammation materials if the course were about
programming in general and the ecology materials if it were more about
data manipulation.
The ecology lessons are still a bit rough around the edges (for example,
the merging data lesson talks about a 'data folder' which wasn't
introduced before, and people still running pandas 0.18 will run into
trouble here and there) but it seems to have worked well with most
complete beginners. I'm not sure how finished the gapminder lessons are
- this page https://swcarpentry.github.io/python-novice-gapminder/ says
'early design stage', I've never taught those materials.
Some notes on Jupyter:
- you may run into the issue of people accidentally changing the 'type'
of their cell from 'Code' to 'Raw NBConvert' and then wondering why
their variables are not created. This happens when you select a cell but
not the textbox of the cell itself and press 'r', in other words, quite
often! In the last 2 days I've seen it happen 3 or 4 times.
- you may run into people struggling with the concept of execution order
of cells, or that you have to execute a cell for changes to be saved.
Feedback from learners was generally positive from what I've seen,
there's less stuff in the way and it's easier to compare what happens
between different computers if you have Mac, Windows and Linux working
together at the same learners' table.You also won't have to teach the
shell or how to use a command line text editor, you can jump right into
python. You can annotate with markdown or raw cells, tab-complete works,
and especially %matplotlib inline is useful to directly show any plots.
Hope this helps
Philipp Bayer
On 10/27/2016 02:22 PM, Jason Bell wrote:
G’day Software Carpentry colleagues
I am planning on running a virtual “python programming” workshop next
week for some of my institutional researchers.
With the resent discussions on the mailing list talking about
alternative python lessons, I am just wondering what the consensus is
with which lesson I should be using to teach python?
This will be the first time I will be teaching the python lesson,
having previous taught the Unix shell and “R for Reproducible
Scientific Analysis”, as well as recently participating in the GIT
lesson. Having done a bit of python programming in the past and
contributed minor source code to some open source projects, I am just
going through the materials and brushing up my python skills as I am a
little rusty.
Anyway, I am writing this message to get some feedback on which lesson
people would recommended for absolute beginners? As currently I can
see the following python lessons:
·Programming with Python -
http://swcarpentry.github.io/python-novice-inflammation
<http://swcarpentry.github.io/python-novice-inflammation> (has this
recently been updated?)
·Introduction to Programming in Python -
https://biologyguy.github.io/python-novice-gapminder/
<https://biologyguy.github.io/python-novice-gapminder/>
·Python as a Second Language -
https://swcarpentry.github.io/python-second-language/
<https://swcarpentry.github.io/python-second-language/> (But I
understand this lesson isn’t really for beginners)!
·Any others I might have missed? I know there are some on the data
carpentry site, but those appear to be domain specific, rather than a
general programming lesson.
I have managed to install anaconda3-4.2.0 on my HPC system today and
will allow my users to use this system if they don’t wish to install
the software on their local computer. I believe this will assist with
people using a standardise setup.
I should note that my experience in programming to date has been more
using a text editor and then running the python interpreter, but I
have been playing around with jupyter today and wondering what the
feedback has been from “beginner programmers” using jupyter compared
to using a text editor and running python manually?
Any feedback and experiences you might have would be greatly appreciated.
Thanks in advance,
Jason
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