I Can Python, So Can You


February 11, 2023

Before I left Aspire, I had started an internal training on Python for those in the org who were interested in learning to do a bit of coding. Inspired by the tag line from an old favorite cooking show from the 1980s, Yan Can Cook, I titled the course “I Can Python, So Can You”.

The scope of that original course was heaviliy inspired by Al Swiegart’s Automate The Boring Stuff With Python and was focused on building skills around office task automation. My colleague, and good friend, Jason Baek helped me design the course and content and we co-taught it during our time there.

When I left Aspire and began work at KIPP Northern California, there was once again an interest from colleagues to learn a little python. So I updated the course, added some additional homework and projects, and taught it once again. I learned a lot that time around and got some great feedback from my data team about the skills they were most interested in (such as learning pandas, APIs, and data visualization).

In the summer of 2019, I was asked by the KIPP Foundation to design a single day version of the course for a data convening in Chicago. I recruited my data engineering colleague, Charlie Bini, to help me build out a streamlined version of the content and to co-teach with me at the convening. We reworked the content to incorporate a more data-focused curriculum and taught it over the course of a six-hour session at the convening. We repeated the course with further updates the following year, although we did it remotely as we were in the midst of the pandemic.

One of the core ideas in all of the incarnations was that the environment setup and IDE learning curve would be a roadblock for many newbies and we didn’t want that to stand in the way of jumping in and learning to code in python. So we reached for Google Colab, which had the ease of Jupyter Notebooks but with an in-browser, cloud hosted environment, and was shareable like any other Google Doc.

I’m hoping to write a new version of these lessons soon, but in the meantime I wanted to get them up on my blog as a reference for folks who ask about learning resources and to hopefully cultivate some additional feedback that I can incorporate in the next iteration.

Without further ado, here are the links to the colab notebooks. I hope they are useful to anyone who finds this post.

I Can Python, So Can You

  1. Introduction
  2. Fundamentals
  3. Control Flow
  4. Loops & Lists
  5. Recap & APIs
  6. Pandas
  7. Course Capstone: Star Wars