This blog is part of the Dashboard Week series. If you want to know more about what the Dashboard Week is, have a look at the first blog in the series .


The last dashboard of the week! We have only about 4 hours to come up with a dashboard, before presenting it in the afternoon, so spending time on making detailed calculations like for the previous dashboard on NHL data was simply out of the question. It was almost like a “Don’t Think, Just React” plus “Keep It Simple, Stupid” sort of situation (to me, at least!)


The Challenge

On the Friday before the 96th Academy Awards, it was timely that our challenge is centred around the awards’ history of nominations and wins, with the data coming from The first thing that came to my mind was the acceptance speech Michelle Yeoh gave when she won her Best Actress Oscar in 2023, in which she delivered a powerful rally call, “And ladies, don’t let anybody tell you you are ever past your prime. Never give up.”.

So I really wanted to investigate if age is an obvious factor for getting an Oscar win or nomination, and if that is consistent across other factors. To do that, I have to find a way to calculate the ages of the people in the year of the ceremony, as TheGoldKnight’s data do not include that. So much for keeping it simple!


The Data

The data from is quite neatly organised in a tabular form, so that’s a positive. Each row represents a nominee/winner in a particular award category, in a particular ceremony year. So far so good!

As previously mentioned, I needed to calculate the ages. My first instinct was to find something from Et voila, there is a publicly available dataset that includes the names of the people in their database, along with their birthdates. As you’d imagine, the information from The Gold Knight does not include the person ID from IMDb, so I was really hoping that most of the names would match. And they did!

However, there were some other niggly bits that did not quite come together as well as I would’ve liked. For one, there were multiple people with the same first and last names (e.g., Robert Smith). I had to make a decision to use the most recently-birthed person as THE Robert Smith. Next, after calculating the ages using the birthdates, there were some instances where the ages were too small or too large (e.g., negative numbers or more than 100). I decided to windsorise the ages to a range of 8 to 86, which represents the youngest to oldest nominees in the history of the awards.

That’s about it! Time to make a dashboard.



In keeping with the “Don’t Think, Just React” mantra, I immediately went for histograms to visualise the distribution of ages, setting the bin size at 5 years to strike a balance between having sufficient details and yet not too hard to digest. To help compare the distributions between any two groups, I stacked the histograms in the same chart and tried dropping the opacity of their fills, but the colours leached into each other and did not look great. As a result, I decided to go with a stepped outline of the histograms instead.

Now onto the design. I wanted a dark background with sparkles, but in my haste I could only find a picture of space with its multitude of stars. That would do, I thought. I just need to dial down the contrast of the fore- and back-ground. I also wanted gold (i.e., yellow) letterings, and then keeping the number of colours used in my charts to two or three colours that do not stand out too much. The end result was, in the words of our head coach Beth, “it reminds me of Star Wars”, and I couldn’t disagree (sad LOL).


What do you think?



J Tay
Author: J Tay