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Dashboard Week: Day 1 – Boating Accidents

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Today kicked off Dashboard Week, and we were handed a detailed dataset on boating accidents across U.S. coastal areas from 2009 to 2023. It covered everything from accident details and vessel information to casualty records, with fields spanning location, weather, injury type, and accident causes. With so much data, our first task was figuring out which story to tell—a fun challenge, but a bit overwhelming with so many potential directions.

The dataset came in four tables, spread over three different time periods (2009-2013, 2014-2022, and 2023). My first step was to read the documentations that came with the data to have a better overview of them. Then, I load everything into Alteryx, where I standardized column names and merged the tables into one cohesive dataset. After dropping fields that weren’t relevant to my focus, I was ready to dive in.

Right away, I was curious about accident causes. I wanted to see if the same factors caused minor incidents as well as serious injuries or fatalities. Digging into the data, I noticed a fascinating pattern: while causes like operator inattention, improper lookout, and excessive speed were often linked to general accidents and injuries, alcohol played a much bigger role in fatal accidents. This finding was eye-opening—although alcohol wasn’t the leading cause overall, it was a top factor in deaths over the years, underscoring its serious impact on safety.

I’ve uploaded the final dashboard on Tableau Public! Feel free to explore and check out the insights for yourself. Thanks for reading, and happy exploring!

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Dashboard Week: Day 1 – Boating Accidents

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

If you are having trouble viewing this article, please report it here. 

Today kicked off Dashboard Week, and we were handed a detailed dataset on boating accidents across U.S. coastal areas from 2009 to 2023. It covered everything from accident details and vessel information to casualty records, with fields spanning location, weather, injury type, and accident causes. With so much data, our first task was figuring out which story to tell—a fun challenge, but a bit overwhelming with so many potential directions.

The dataset came in four tables, spread over three different time periods (2009-2013, 2014-2022, and 2023). My first step was to read the documentations that came with the data to have a better overview of them. Then, I load everything into Alteryx, where I standardized column names and merged the tables into one cohesive dataset. After dropping fields that weren’t relevant to my focus, I was ready to dive in.

Right away, I was curious about accident causes. I wanted to see if the same factors caused minor incidents as well as serious injuries or fatalities. Digging into the data, I noticed a fascinating pattern: while causes like operator inattention, improper lookout, and excessive speed were often linked to general accidents and injuries, alcohol played a much bigger role in fatal accidents. This finding was eye-opening—although alcohol wasn’t the leading cause overall, it was a top factor in deaths over the years, underscoring its serious impact on safety.

I’ve uploaded the final dashboard on Tableau Public! Feel free to explore and check out the insights for yourself. Thanks for reading, and happy exploring!

Share this post