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Dashboard Week: Day 4 – Dengue Fever

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.

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Today’s challenge for Dashboard Week took me into the world of dengue data, using the dataset from OpenDengue. This dataset provided a snapshot of global dengue cases, with Brazil standing out as the country with the highest number of cases. But the dataset was pretty limited as it only included case counts and basic country information. So we were asked to bring in some extra data to make a story for our dashboard.

At first, I wasn’t sure what other data would be relevant. I tried exploring several options in Alteryx, looking into factors like healthcare access, population density, climate conditions, and poverty levels. Eventually, I decided to zero in on poverty. Brazil’s poverty levels have decreased over time, but dengue cases remain high, so I thought there might be an interesting connection to explore. Once I gathered my poverty data, I jumped into Power BI to create the relationship between poverty rates and dengue cases. I set up my data model to join the poverty data with the dengue dataset, using country as the key to link them. This allowed me to explore trends between poverty levels and dengue prevalence across various countries, with Brazil as the focal point.

The results? Nothing exactly like what I expected! Unfortunately, my analysis didn’t give me the clear answer I was hoping for. I went into this project expecting to find a straightforward relationship between poverty levels and dengue cases in Brazil. After building out the data relationships and trying different charts, the connection between poverty and dengue turned out to be more complicated—and not as direct as I thought it might be.

It was one of the tougher challenges I’ve faced, as I hit several roadblocks trying to find the right data connections and meaningful insights. From wrangling multiple datasets in Alteryx to experimenting with visualizations in Power BI, I encountered unexpected complexities that pushed me to explore different angles. But in the end, everything came together, even if the insights weren’t as clear-cut as I’d initially hoped. Here’s a snapshot of part of the final dashboard.

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Dashboard Week: Day 4 – Dengue Fever

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’s challenge for Dashboard Week took me into the world of dengue data, using the dataset from OpenDengue. This dataset provided a snapshot of global dengue cases, with Brazil standing out as the country with the highest number of cases. But the dataset was pretty limited as it only included case counts and basic country information. So we were asked to bring in some extra data to make a story for our dashboard.

At first, I wasn’t sure what other data would be relevant. I tried exploring several options in Alteryx, looking into factors like healthcare access, population density, climate conditions, and poverty levels. Eventually, I decided to zero in on poverty. Brazil’s poverty levels have decreased over time, but dengue cases remain high, so I thought there might be an interesting connection to explore. Once I gathered my poverty data, I jumped into Power BI to create the relationship between poverty rates and dengue cases. I set up my data model to join the poverty data with the dengue dataset, using country as the key to link them. This allowed me to explore trends between poverty levels and dengue prevalence across various countries, with Brazil as the focal point.

The results? Nothing exactly like what I expected! Unfortunately, my analysis didn’t give me the clear answer I was hoping for. I went into this project expecting to find a straightforward relationship between poverty levels and dengue cases in Brazil. After building out the data relationships and trying different charts, the connection between poverty and dengue turned out to be more complicated—and not as direct as I thought it might be.

It was one of the tougher challenges I’ve faced, as I hit several roadblocks trying to find the right data connections and meaningful insights. From wrangling multiple datasets in Alteryx to experimenting with visualizations in Power BI, I encountered unexpected complexities that pushed me to explore different angles. But in the end, everything came together, even if the insights weren’t as clear-cut as I’d initially hoped. Here’s a snapshot of part of the final dashboard.

Share this post