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Dashboard Week #5 – Harry Potter

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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For the final day of Dashboard Week, we were given a light and enjoyable dataset for once, no more depressing data about the state of the world or how crime and inflation are going up. Instead, we were given information on the Harry Potter films. The various sheets included information on the characters, locations, lines of dialogue, and some of the spells. The main downside of the information was that there was no actual information on the spells themselves, no data whatsoever on who cast what spell and what was the target, and so the only real way to try and get any amount of information is appending the spells sheet onto the dialogue and using the Contains formula to narrow down to what characters mention a spell by name. The issue with this method is that it just looks at the line of the dialogue with no context, so if they just mention the spell in passing but didn’t cast it, it would still be flagged, but for the purposes of time constraints it would have to do.

Now that I had my data, I actually realised I need to pull in some additional sheets to further my idea. So I grabbed a more extensive sheet of characters in the franchise, now with extra information such as their blood type, which would be really useful in breaking down the four houses. So with all that information acquired I started building out my dashboard.

The focus of my dashboard was on the four houses and the demographics that made up their cohorts, what gender were their students, did they favour any blood types or species, and how many times or often did someone from any house cast a spell. These were all able to be done as a result of the extra data I had brought in, as well as the work done in Alteryx to append the information. I was quite happy with the visuals of the dashboard at the end, particularly with regards to how I used custom shapes as images in my pie charts to really make them pop out.

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