When applying for the Data School Down Under, the first challenge you’ll face is deciding on a topic for your first Viz.

It can be a bit overwhelming. There are so many datasets on the internet – with more being added every day. The possibilities are limitless – so, which is the right one to download and start exploring as part of your application?

For those of us in the most recent cohort (#DSAU9), it came down to passion and interest. The only thing they had in common was how different they were from each other. You can scroll down to see the Viz’s we used for our applications below.

Starting with an interest close to your heart is a great steppingstone for your first Viz. It will encourage you to dig deeper into the data, and put in the effort needed to create a great first visualisation. It also means you can utilise your existing subject knowledge to enhance your presentation.

As an extra hint, an easy way to start learning how to create impressive visualisations in Tableau is to check our Andy Kriebel’s #MakeoverMonday YouTube series. Andy is the head coach of The Data School UK, and each week he takes a new dataset and turns it into a new visualisation – the videos last about an hour and a packed with all the knowledge you’ll need to start on your Tableau journey.

Now – onto the Viz’s we created for our Data School applications.


Binbin Chen: What factors contribute to a low Airbnb rating in Australia?


For his application, Binbin sourced and analysed data on Airbnb reviews in Australia, analysing the data through a number of different chart types. You can read more about Binbin’s decision to join the Data School in this blog.


Rey Lin: How do the worlds COVID-19 vaccination programs compare?


For her application, Rey chose to analyse data about COVID-19 vaccination rates across the world because she “just wants to get rid of COVID”. Her use of maps, bar charts and area graphs makes for an engaging presentation of this data.


Quoc Thang Nygen: How can new home buyers use data to help them find the perfect home?


Thang’s application is really cool, a Viz to assist new home buyers find an ideal first home by using other data about each suburbs liveability. “I chose this because i want to make a guide for 1st home buyer to choose their first property,” he said. Read more from Thang about his journey into the data school here.


Kier Bituin: What can data tell us about the Minnesota Timberwolves?


For his Viz, Kier (a basketball fan) chose to analyse the performance of the Minnestoa Timberwolves in 2010. “They are my favourite basketball team in the NBA and I wanted to reflect on the players who had a big influence on the team during that era,” he said. Kier’s also been generous enough to write up his tips on applying for the Data School, make sure to give them a read.


Eric Shang: Can You Make A Pokedex Using Tableau?


Eric had one of the more creative applications for the Data School, using Tableau to create a Pokedex. He was driven equally by his interest in the Pokemon, and his desire to test the limits of Tableau’s functionality, saying “I chose to do a Viz on Pokemon, because I’m a Pokemon Game fan and I want to have some fun with this Pokemon data set that I found.”


Azin Fakour: What does the data tell us about the Melbourne housing market?


For her application, Azin used Tableau’s graph and chart functionality to analyse the Melbourne housing market from a number of different perspectives. “I started to learn Tableau through online courses and prepared myself to create an eye-catching VIS for the first step of the application. I sent the first sketch of my VIS one week before the deadline to get feedback from the Data School team,” she wrote describing her tips for the application process.


Kieran Adair: How influential is your vote?


I’ve always been interested in the intersection of politics and data. For my application, I chose to explore how different population sizes in different electorates around the country can skew election results – and used Tableau’s functionality to tell a story along the way.

Final note, as you can see the successful #DSAU9 candidates all chose widely different datasets and topics to work from – and took different approaches in their use of Tableau. I hope these examples inspire you to also choose a topic you’re interested in, and find out how Tableau can help you explore and visualise it. Now, go forth and Viz!

Kieran Adair
Author: Kieran Adair