Go right to the zine (print it duplex, flipping on the long side, and then cut the pages in half horizontally. The pages should fold together in the correct order)

I attended my first PyCon last spring. I decided that if I were to go to that conference again, I would want to try something new that I hadn’t done the first time. I thought maybe I would like to try presenting something. A couple days before the submission deadline I discovered that they were accepting poster proposals. I threw one together for a project I’d been noodling on but wasn’t quite sure how to do, and it was accepted!

So now I have to finish my project. The idea I had been noodling on was creating some type of fun resource for others like me who are research and data people interested in learning to program. I’ve had a really challenging time and I wanted to think more deeply about why it has been so hard, and what might make it easier.

I had the opportunity to go to a prototyping workshop (notes are here) at Geo:Code 2.0, a local hackathon focused on geospatial data, so I used what I learned there to put together a problem and solution statement for my project. Here it is:


Non-programmers interested in learning programming as a tool to help them work with data need a way to identify appropriate learning resources because there’s so much out there it can be daunting.


My solution is an interactive resource like an internet quiz that:

  • Collects information on what/why learner wants to learn programming, learning preferences, and current experience
  • Recommends types of resources that might be helpful and ways to identify and locate them
  • Directs the learner to some actual example learning resources for learning Python and R

My vision is that the tool gives you a place to start and some help to map out a path to get from where you are to where you want to be. Maybe some terms to google, some examples of types of things to look for, some guidance.

I read this statement to some folks at Geo:Code 2.0 as well as some data colleagues of mine to see what they thought and I refined it a little. Then I started trying to plan out the resource, but I realized that there were a lot of pieces and it was challenging to try and plan them all at once. I got stuck!

I was staring instead of working when I remembered that at PyCon last year, a local Montreal PyLady I met (Julia gave me a copy of a technical zine she wrote about strace. I have no idea what strace is or why you would use it, but I love that she made a zine about it! I told her I owed her a zine, and I daydreamed about a zine exchange at PyCon for this year. I tried to write a zine about my experience at PyCon but never got very far.

In any case, this was all I needed- inspiration! I could make a zine that illustrates the problem that my project aims to address. I am still on a bumpy winding path to learning programming, and I want to create the resource that I didn’t have. What better way to start than to tell the story of my bumpy winding path!

So using my idea to create a “Choose Your Own Adventure” style thing, I made a little adventure zine about trying to build skills for data analysis. You can download the print version here. The zine walks through each of the things I’ve tried and explores the pros and cons I’ve experienced with the options. Some of the stories are embarrassingly true and unfortunately not exaggerated (like the one where I become an organizer of a local user group!).

What I’m working on now

Sharing this with anyone who’s interested, and hoping to gather feedback on two things: what challenges others have faced while trying to learn programming for data analysis, and what would be useful to go into the tool in terms of both content and structure. I am planning to bring my zine to the Data Analysts for Social Good conference Do Good Data in Chicago, as well as a local all-women hackathon Hack the Gap here in Minneapolis.

Here’s the note I include at the end of the zine, reflecting on what some of the challenges I encountered are. If you are interested, please contact me! I would love to hear from you. You can email me at john3718@umn.edu or tweet me at @roxLjohnson. I hope you enjoy it! It was really fun to make :)

I’ll post an update after PyCon with what I ended up presenting at the poster session. I’m really looking forward to going to the conference with the purpose of hearing about learning tips from people who use Python all the time!


There are so many cool things you can do to obtain, clean, analyze, explore, and visualize data if you know some programming. I’ve been a research analyst for about five years, and actively trying to learn Python and R for data analysis for about two years. I’ve found it extremely challenging and I’ve met many others with similar experiences. This zine is based on the true story of my own learning experience and aims to highlight some of the challenges I continue to face:

  • Not knowing where to start or what the big picture looks like; no clear view of what’s possible or a map of how to get there

  • The vast number of resources is daunting; it is difficult to find resources that are both at my skill level and relevant to what I want to learn

  • Not knowing what I don’t know- lacking the awareness that I don’t understand a core concept, and not being able to articulate questions or search terms

  • Many resources are intended for people who are or want to become developers or professional programmers, which isn’t me

  • I don’t feel like I fit anywhere- other researchers may not know about programming, people who regularly work with data in Python sometimes Excel-shame me, and programmers tell me that learning Python is easy

Reflecting on my experience and talking to other data people about their challenges, I decided to create a tool that will help data people start to explore the world of programming for data analysis. The tool starts with an assessment of why you want to learn programming, what your current skills are, and how you like to learn. It will then recommend ways to find relevant resources and direct you to some examples of actual resources.

I am looking for stories of other data people working to learn programming, and also feedback on the development of my tool! If you are interested, let me know!