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Dashboard Week #2

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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Dataset Overview:

Today is the second day of the dashboard week, we were given a  dataset about mines. It covers 1171 individual mines in 80 different countries, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included.

 

Plan and Insights:

My goal is to tell a story about the features of the top countries that sell mineral commodities, whether they have anything in common, and their performance on some KPIs compared to other countries.

The insights I glean from the dataset show that the top countries tend to have high reserves of minerals, but they do not necessarily have high processing capacity. Additionally, they have better control over the waste generated during the mining process.

I used a bar chart to compare different KPIs between top countries and other countries, a line chart to show the yearly sales trends for different country groups, and a donut chart to display the mining skills related KPIs.

 

Table Schema:

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