This post is the last in the data science series (see 1, 2, and 3). Here we’ll wrap up the entire discussion, including whether we actually discovered anything about our initial question regarding “Which is the best Pokémon character?”
This post follows on from the first. Here we’ll focus on the skill of interpreting our Pokémon data so you trust the data enough to make decisions based on it. This is fairly important if you are a tester working in any context wherein actions against data will be presented to you or where you have take those actions yourselves as part of the testing.
I’ve talked a bit about testers and AI as well as testers and machine learning. Here I want to focus a bit on one area that can be a basis for both of those areas: data science. As a tester, you don’t need to be a data scientist. But it certainly doesn’t hurt to have a grounding in what data scientists do. Here we’ll do some exploratory computing with Jupyter; we’ll use some numerical and visualization libraries and we’ll explore the (fascinating?) world of Pokémon data. So let’s take a few posts to dig into this.