I generally don't teach coding to newbies, so a lot of the coding I teach is correcting poor mental models and teaching features (somewhat) unique (or different) in Python.
My best Pandas courses have been when the client opts to use their data for the course (instead of my canned data). The students are already subject matter experts with the data and when they learn some of the tricks to slice and dice, summarize, and visualize, they are off to the races. They dig right in.
Teaching as the article suggests is very difficult because examples that appeal to some or boring or confusing to others. I'm not saying it won't work, but there are cons as well. When I'm teaching with my "canned data", I try to mix in a few different datasets from different areas so students can see that the ideas are generally adaptable.