# SciPy 2019 Post-Conference

written by Eric J. Ma on 2019-07-15

It’s my last day in Austin, TX, having finished a long week of conferencing at SciPy 2019. This trip was very fruitful and productive! At the same time, I’m ready for a quieter change - meeting and talking with people does take a drain on my brain, and I have a mildly strong preference for quiet time over interaction time.

## Tutorials

I participated in the tutorials as an instructor for three tutorials, which I think have become my “data science toolkit”: Bayesian statistical modeling, network analysis, and deep learning.

Of the three, the one I had the most fun teaching was the deep learning one. The goal of that tutorial was to peel back a layer behind the frameworks and see what’s going on. To reinforce this and make it all concrete, we live coded a deep learning framework prototype, and it worked! (I didn’t plan for it, and so I was quite nervous while doing it, but we pulled it off as a class, and I think it reinforced the point about revealing what goes on underneath a framework.

I also had a lot of fun teaching the Bayesian statistical modeling tutorial, which I had co-created with Hugo Bowne-Anderson, and as always, my personal “evergreen” tutorial on Network Analysis always brings me joy, especially when we reach the end and talk about graphs and matrices. I think the material connecting linear algebra to graph concepts is one that the crowd enjoys, and I might emphasize it more going forth at the SciPy tutorials.

## Talks

This year, I delivered a talk on pyjanitor. Excluding lightning talks, this is probably the first time I’ve started my slides one day before having to deliver it (yikes!). Granted, I’ve had the outline in my head for a long time now, I guess having to do the talk was good impetus to actually get it done.

Apart from that, there’s a rich selection of talks at SciPy from which I think we can screen at work over lunches (Data Science YouTube). I particularly like the talk on Optuna, a framework for hyperparameter optimization, and I think I’ll be using this tool going forwards.

## Sprints

I did a sprint on pyjanitor with my colleague Zach Barry. This sprint, we had about 20+ sprinters join us, the vast majority of them being first-time sprinters.

One thing that stuck for me, this time round, is how even first-timers have different degrees of experience. Some know git while most others don’t; most don’t have any prior experience with Gitflow. I had an interaction that led me to realize it’s very important to state meaningfully what “beginner” means in concrete terms. For example, a “beginner” pyjanitor contributor is probably a pandas user, may or may not have used git before, probably doesn’t know GitFlow. A common prerequisite quality amongst contributors would probably be that they would have the patience to

1. Read the documentation,
2. Attempt at least one pass digesting the documentation, and
3. Ask questions regarding the intent behind something before asking for a change.

In terms of the things accomplished at this sprint, contributions mainly revolved around:

• Improving language in the docs,
• New functions, and
• New example notebooks.

In addition to pyjanitor sprinting, special thanks goes to Felipe Fernandes, who helped me get jax up onto conda-forge! SciPy is really the place where we can get to meet people and get things done.

## Career advice learned

While at SciPy, I had a chance to talk with Eric Jones, CEO of Enthought. Having described my current role at work, he mentioned how having a team like the one I’m on parked inside IT gives us a very unique position to connect data science work across the organization to the consumers of our data products. When I raised to him my frustrations regarding our infatuation with vendors when FOSS alternatives clearly exist, his advice in return was essentially this:

Focus on leveling-up your colleagues skills and knowledge, keep pushing the education piece at work, and don’t worry about the money that gets spent on tooling.

Having thought about this, I agree. Over time, we should let the results speak. At the same time, I want to help create the environment that I would like to work in: where my colleagues use the same tooling stack, are hacker-types, aren’t afraid to dig deep into the “computer stuff” and into the biology/chemistry, and have the necessary skill + desire to design machine learning systems to systematically accelerate discovery science.