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!

Veronika Varaksina
Author: Veronika Varaksina

Meet Veronika, a dynamic and adaptable individual with a diverse background in economics, accounting, finance, and data analytics. Veronika pursued a Bachelor’s degree in Economics and gained valuable experience in financial analysis, budgeting, and forecasting while working for five years in accounting and finance. However, she soon realized her passion for data analytics and decided to pursue a postgraduate degree in Analytics at Victoria University. Throughout her academic journey, Veronika honed her skills in data visualization, statistical modeling, and machine learning. Her expertise earned her a spot in the highly competitive Data School program, where she further continues to expand her skills in data analysis.