Introduction

Today’s dashboard was one of the hardest so far.  Not because of the dashboard and not because of getting the data but because of the data quality, or what data wasn’t there.  We were sent to the UN’s website to look at there 17 SDGs and choose at least one to make a dashboard on.  Sounds easy right? Wrong.  I spend most of the day cycling through datasets to find one that was complete and not a completely upsetting topic as yesterday I focused on the worst cities in the world, I wanted something a bit more cheerful.  When I finally settled on the education data set I was able to find some interesting insights on Panama, a country with a dropping attendance rate but growing literacy rate.

 

Alteryx

Luckily today’s data was reasonably clean, it was just a lot of the columns where missing multiple values.  I needed to transpose and crosstab the data to get it into the shape I wanted but apart from that there was no other cleaning required.  Once I had my downloaded data set clean enough, I joined on some population data that I could use as a reference for some of the education numbers.  My workflow looked like this;

 

 

The Dashboard

My first chart shows just how much of an outlier Panama is compared to the rest of the world, I grouped the average for all countries in the world and compared it to Panama’s for % of children out of school.  This shows that initially Panama follows the same decrease that the world does however around the mid-early 2000’s it starts to rise eventually reaching the 3rd worst in The Americas in 2017.

 

I wanted to look into what affects this might have had and to my surprise I saw that the national literacy rate is actually still increasing even though attendance is dropping.  That is another way that Panama behaves as an outlier, as the plot on the right shows, there is a negative correlation between attendance and literacy rate.  Meaning that in general, countries with a higher rate of students missing school have a worse literacy rate.  Panama is the red dot on the plot and seems to be right in the mix of all other countries, however a look at their literacy rate and attendance over time shows a different story.

 

The next part of the dashboard looks at the gender imbalance in Panama.  While it is not very extreme, there is a clear difference between Male and Female literacy and attendance in Panama.  Males are missing school at a lower rate than females on average.  Over time that has also been consistent with the worst separation being around 2005, the same time the attendance starts to drop for both genders.  Literacy is consistently lower in Females up until 2020 when they are about equal however that is because of male literacy dropping, not female literacy increasing.  Looking into age groups, while females overall have a lower literacy rate than males, young females actually have a much higher literacy rate than adult males which would make sense seeing as both genders have seen a steady increase in literacy over the years.

 

The final part of the dashboard focuses on the future and how the gender imbalance may have a better future ahead of itself.  I looked at the amount of teachers trained to teach primary school and that again is consistently lower in females until around 2015 when it evens up.  This could help explain the increase in female literacy compared to males in the later years.  On a surprisingly good note, females have a much higher secondary school completion rate and that is consistent over time.  This is not a count of students that complete secondary school but a percentage so it means that while there is lower attendance amongst female students, those that do attend school see it through to the end at a higher rate than males.

 

Conclusion

I definitely struggled with this days dashboard, putting it together wasn’t too hard but finding useful, happy data that was complete was a challenge.  Once I had done that though, the dashboard came together nicely and the insights were okay too.  I’m happy to be onto the last day of dashboard week and onto Tableau for the final day.

Mikael Nuutinen
Author: Mikael Nuutinen