Teaching Data Science withOUT Jupyter notebooks
Teaching Data Science classes it is indeed possible without Jupyter notebooks. And there indeed exist so many alternative options. A shiny deck of slides, maybe even full of animations too. Or you could opt for a more standard PDF of lecture notes that students,
especially undergraduate, would find much more useful to study with. And when it comes to work on some actual code for exercises or assignments, there is always a GitHub repository one could use, with scripts|assignment.pyfiles that students could definitely read, comprehend, and run on their own. So, sure! Teaching data science classes withOUT Jupyter notebooks is indeed possible.
It's just not worth it.
Not using notebooks definitely requires much more work in preparing the materials; sharing the materials with students is detrimental; and most of all, the learning experience is completely ruined, choosing boring and static over entertaining and interactive.
In this talk, I would like to share my journey into empowering data literacy using Jupyter notebooks. I will share the many lesson learnt through the years of teaching to graduates and undergraduates at Uni, as well as to data science professionals and practitioners. Practical tips and tricks will also be shared, showcasing the the many tools (e.g. jupytext, nbdev, rise) and the environments (e.g. colab, mybinder) in the Jupyter ecosystem that I found fundamental to improve the learning experience.