W1D4 - Advice for data scientists

Twitter API

It's very convenient how a HTML/CSS/JS project on repl.it is automatically hosted, so there's no need to explicitly set up a back end (Hello World example here, viewable here).

I've been wanting to play around with Twitter's API, and thought setting up the client JavaScript to make the API call keeps things simple. Annoyingly .env files for HTML/CSS/JS projects get exposed and the more interesting parts of the API is behind the auth.

OK so I'll need my own back end. It turns out this is not too complicated. I stitched together a minimal Flask web server that dumps the response in the HTML (project here, viewable here).

Content: Data science

Today I also presented on applying machine learning to payments use cases - this flows nicely to the content of the day.


I came across this Stitch Fix blog post when I was first interviewing for a data science role. The advice to choose (or perhaps, give considerable weight to) a company on whether data science makes-or-breaks the business is fantastic - if it's any one article I'd recommend on the topic, it's this.