[rob_austin]

Some example topics that took me a lot of reading from different sources to understand.

- how to write a python library that you can host for public/private use

- adding test coverage to data science python projects

- learning libraries like matplotlib, seaborn beyond what you see in tutorials

I think material for all fo this exists in different sources like documentation/stack over flow but either it's too detailed or too superfluous. The middle (intermediate) layer is often missing.

[webartifex]

I get you.

The plotting tools I would actually consider in an "Intro to Data Science" course, not in an "Intro to Programming".

I started to write a library implementing Gilbert Strang's Linear Algebra book assuming nothing but core Python. What inspired me to do that was reading Philip N. Klein's book "Coding the Matrix". I thought writing a LA library for fun and study purposes makes sense if you want to go on and study data science. However, that project is not yet ready to be published. Would that be what you are looking for?

[rob_austin]

I agree with you about the tools being in a data science course. What I'm pointing out is a lot of people have spent energy creating intro to programming courses but similar amount of effort has not been spent in creating intermediate courses that help people become proficient with the tools of data science. It's usually "intro" level content or library documentation.

I think writing code really helps you understand the algorithms in more depth so I'm all for exercises for LA like you suggest.