Tableau is Powerful! Olympic Swimming Medal History Visualized
Data visualization project for my application for Data School
What This is About
I am a Data Analytics student, quite a beginner. This Covid time has led me to explore a career path that I can learn from home, and I discovered data analytics. I began my journey with the Google Data Analytics Professional Certificate along with a number of Udemy courses on Excel, SQL, PowerBI, and Tableau. Now that I feel somewhat confident in using my skills in real data analytic tasks, I started job searching and I found Data School. (Here)
Data School (Australia) is “a paid two-year long immersive course which will create the next generation of great data analysts and consultants.” And I decided to apply for it. It seems to be a great opportunity to learn from data analytic experts and gain some real experience in the field.
Tableau seems to be the main tool that Data School is specialized in. In fact, the application process starts from the applicant uploading data visualization in Tableau Public.
So, I decided to apply for the course. For my visualization, I chose “Summer Olympics Medalist Dataset” as the data set thinking I would like to visualize the medal history of Olympic swimming. Well, it is because summer is coming soon in Australia and I was curious as to what kind of visuals I could create with this data set.
However, I changed my mind to summit different visualization at the last minute thinking this might not be enough. I will post about the other one soon.
Let’s Get Into It
I downloaded the data set that has “every summer Olympic medalist from 1896–2012. Criteria such as home country, event, medal, and gender are included in the data. Courtesy of The Guardian.”
The data looked quite clean at first until I realize there are some country codes that were in the “ALL MEDALISTS” table which were not in the “IOC COUNTRY CODES” table. (countries that do not exist anymore lie East and West Germany or the Soviet Union) This would create a problem as I was going to add a country column with the full country name for Tableau to recognize. I had to find a country name for some country codes and add in the “IOC COUNTRY CODES”.
Another issue was that the data set did not include data up to date — it contained data until 2008. I thought it wouldn’t be as interesting as it could be if I don’t use recent/relevant data. So, I found another data from 2012–2020 on another website and created another data set in order later to append them in the power query.
After I prepared the data for my use, I cleaned it in the power query. I filtered out other disciplines so I could work on only swimming; added and removed columns; changed the event names using replace values as they were not consistent; and formatted the athlete name column.
I used to prefer Power BI over Tableau. But as I was working on this project, I learned more features, and now I am absolutely loving Tableau. Perhaps because I am still a beginner, what Tableau can do with data really fascinates me. I definitely would like to work on Tableau more to learn more.
With the data set, I created an active, interactive dashboard with over 15 sheets that visualize the whole history of the Olympic swimming medals with different styles.
Using Canva, I created a little Olympic swimming logo to put on the start.
Followed by the “Did You Know” section hoping to catch the readers’ interest.
Then, I put one of the most overall information “How Many Medals Have Been Awarded To Date?” It illustrates the running sum of the total medal counts each year, which shows that Olympic swimming consistently continued and it alone contested over 3000 medals which are quite impressive. Hovering over the bars tells the exact number of the medal count with the tooltip “Total medal up to year”.
Then comes the next map visualization that displays the medal distribution trend on the map. With filters and a highlighter, one can look at a specific period with a specific country highlighted.
As shown, it is easy to see the number of medals won by athletes from a country. Hovering over different pie angles will provide with tooltip telling how many specific medals are won. The map above displays how many bronze medals were won by Australian athletes in the 2020 Olympics.
This line chart shows countries' medal trend over time. It is also the running sum of the medal count.
Viewers can select specific countries to compare their trends over a certain period. As displayed above, one can observe the performance trend of each country with this visual.
Then comes a few charts that visualize the number of medals in different forms. The packed bubble chart, as impressive as it looks, provides such a visual impression of the number of medals. Then the bar chart displays the percentage of medal type with rough number scales. Below the filters on the left, there are two numbers that tell the number of medals earned and the number of athletes who earned those medals. The number of athletes is counted distinctly meaning that athletes with multiple medals are still counted 1.
The top 10 countries show countries with the most medals in the selected period.
Next, I put athletes with the most medals (top 3 in each gender). I include their country in the tooltip and I was rather surprised that they were all Americans!
I then put a table chart that displays the medal detail of athletes with country information. Specific search is made easy with year, country, and the swimmer name wildcard filter.
My wife found this map cute and had a little laugh at it. It displays the number of swimmers per country by both color and size. I feel like it is a bit overdone but also quite like it for its cute style.
In fact, if you were to look at the map closer, you see the effectiveness of this visualization.
Last but not least, is the heatmap ish table that illustrates the number of medals contested in different events each year. It shows which events have ceased, re-started, or newly introduced and continue.
I hope you enjoyed my visuals. I myself enjoyed it quite a lot as it was a fun theme to work with and I was able to turn raw data into something that displays information in structured, relevant, and easy-to-understand visuals.
This project was another learning journey where I put theories into practice. From putting the right dimensions and measures to create visuals I want, formatting filters, sheets, axis, highlighters, maps, making donut pie charts to structuring the dashboard, I learned so much. Perhaps it’s because I am only on a beginner level, but learning Tableau is so exciting and fun.
Please leave a comment if there is a place where I could improve anything. I am quite keen to be part of Data School. I think it is a great opportunity. As far as I know, Data School is located in UK and Germany as well. Maybe Data School is for you and you might want to check out.
All the best on your data journey and I hope to keep writing my posts and learn together.
I will post the visualization that I will apply to data school soon.