One challenge I’ve run into as an analyst was in keeping track of analyses, sharing them with my team, and reproducing them at a later time. Despite my best efforts, the analyses I did were often scattered across ad hoc queries and spreadsheets and plotting tools. I would look back at a visualization from months ago and not remember how I got there.
Enter iPython notebooks. When I joined Jana, I was excited to hear that the Jana data team was fully on board with iPython notebooks. The team has a github repository where we store iPython notebooks that various analysts, PMs and engineers across the company are working on. As a n00b at Jana, this was exciting for a couple of reasons:
- Onboarding is easier when you can see what kinds of analyses other people have done or are working on. I can see that my co-worker already looked at different ways a product feature could have impacted retention. I can now go back to his notebook, duplicate and build upon it with different techniques or data sources. It’s a great way to learn from your co-workers and internalize product takeaways that others have explored.
- It’s easy to rinse and repeat analyses that you know you’re always going to do. With A/B tests, there are a set of business metrics we’ll always to track against. We have a iPython template notebook for experiments where we can just update the experiment name and run it through to get all the plots and tables we’d want in analyzing an experiment.
- There’s an active community of iPython notebook users doing cool and interesting things.
You can get started with iPython notebooks for your data team here!