Data scientists need to learn JavaScript

Moving quickly

Over the last few months, I've become very interested in rapid prototype development for data science projects. Here's the key question I asked myself: how can a data scientist build their own app as quickly as possible? Nowadays, speed means code gen, but that's only part of the solution.

The options

The obvious quick development path is using Streamlit; that doesn't require any new skills because it's all in Python. Streamlit is great, and I've used it extensively, but it only takes you so far and it doesn't really scale. Streamlit is really for internal demos, and it's very good at that.

The more sustainable solution is using Django. It's a bigger and more complex beast, but it's scalable. Django requires Python skills, which is fine for most data scientists. Of course, Django apps are deployed on the web and users access them as web pages.

The UI is one place code gen breaks down under pressure

Where things get tricky is adding widgets to Django apps. You might want your app to take some action when the user clicks a button, or have widgets controlling charts etc. Code gen will nicely provide you with the basics, but once you start to do more complicated UI tasks, like updating chart data, you need to write JavaScript or be able to correct code gen'd JavaScript.

(As an aside, for my money, the reason why a number of code gen projects stall is because code gen only takes you so far. To do anything really useful, you need to intervene, providing detailed guidance, and writing code where necessary. This means JavaScript code.)

JavaScript != Python

JavaScript is very much not Python. Even a cursory glance will tell you the JavaScript syntax is unlike Python. More subtly, and more importantly, some of the underlying ideas and approaches are quire different. The bottom line is, a Python programmer is not going to write good enough JavaScript without training.

To build even a medium complexity data science app, you need to know how JavaScript callbacks work, how arrays work, how to debug in the browser, and so on. Because code gen is doing most of the heavy lifting for you, you don't need to be a craftsman, but you do need to be a journeyman.

What data scientists need to do

The elevator pitch is simple:

• If you want to build a scalable data science app, you need to use Django (or something like it).
• To make the UI work properly, code gen needs adult supervision and intervention.
• This means knowing JavaScript.

(Data Scientist becoming JavaScript programmer. Gemini.)

In my view, all that's needed here is a short course, a good book, and some practice. A week should be enough time for an experienced Python programmer to get to where they need to be.

What skillset should data scientists have?

AI is shaking everything up, including data science. In my view, data scientists will have to do more than their "traditional" role. Data scientists who can turn their analysis into apps will have an advantage. 

For me, the skillset a data scientist will need looks a lot like the skillset of a full-stack developer. This means data scientists knowing a bit of JavaScript, code gen, deployment technologies, and so on. They won't need to be experts, but they will need "good enough" skills.