Many of us in the SciPy community teach scientific computing with Python. This BoF will be an opportunity for those of us involved in teaching SciPy to discuss best practices, common idioms, teaching techniques, and presentation tools. Hopefully this will lead to the start of a community-developed set of courseware for teaching the scipy stack.
Many of us in the SciPy community teach scientific computing with Python. This is done in many formats: bootcamps, format courses, in-house workshops, online tutorials etc. While there are great number of open source course materials available, many of them derived from one another, there is no one source of nicely curated ind consistent materials.
There is also a a fair bit of variation of seemingly small stylistic details that can lead to confusion for newbies. Examples are how to import numpy, whether to teach the MPL pyplot interface or OO interface, etc.
This BoF will be an opportunity for those of us involved in teaching SciPy to discuss best practices, teaching techniques, presentation tools, etc. Hopefully this will lead to a community-developed set of courseware for teaching the SciPy stack.
Some possible topics of discussion:
And the big question:
I've been using numpy/scipy since Numeric version (?) or so, around 1998. Since that time, I've done a lot of informal teaching and tutoring. More recently, I've been teaching Python for more general use for the Univ. of Washington continuing Eduction Program, CodeFellows, inc., and few in-house trainings as a private consultant.
When asked with introducing the scipy stack, I've found a lot of great open source materials out there to borrow from, but no one source of internally consistent, comprehensive materials to "just use". I'd love to work with the community to create such a set of materials, but even if that is not realistic, a chance to get together and share tips and techniques would be great.
You can see some of my stuff on GitHub here: