The final day of Dashboard week presented us with a new set of data on the Grand Prix. As someone who knows next to nothing about car racing, I was initially intimidated by the dataset. However, I decided to take on the challenge and began by asking chat GPT some questions, which gave me some initial insights into how the Grand Prix works. I learned that drivers earn points based on their placement in the race and that the sport involves a lot of collisions.
Initially, I planned to analyze lap times and identify which drivers had the quickest laps. However, I realized that the dataset was more complex than I had anticipated, and I was running out of time. As a result, I decided to focus on a simpler topic: identifying which drivers had the most injuries.
Unlike my previous work with Alteryx, I created my dashboard using the relationships in Tableau. While I wished I had more time to join 3-4 tables before starting, I made use of the resources I had which is Tableau and built a dashboard with the following connections:
And following elements:
- A filter to display the number of collisions per Grand Prix and identify collision participants.
- A pictograph that showcases drivers who had crashes.
- A KPI for the average number of collisions per driver, calculated using a LOD.
- A KPI for the overall number of collisions per driver.
- A heatmap showing the nationality and constructor of each driver.
Although my knowledge of the sport was limited, I recognized the name Schumacher, who had many accidents and is one of the most well-known drivers.
While I must admit that my final product was not perfect, I am proud of the work I put into it, given the time constraints. There are still areas that I can improve, and I am excited to continue working on my skills and techniques in future projects.
In conclusion, I hope you found my story about the visualization interesting, and I am excited to tackle new challenges in the future. It is time to finish the Dashboard week!
