Long-time reader Daniel L. isn't a fan of this chart, especially when it is made to spin, as you can see at this link:


Like other 3D charts, this one is hard to read. The vertical lines are both good and bad: They make the one dimension very easy to read but their very existence makes one realize the challenges of reading the other dimensions without guidelines.

This dataset allows me to show a ternary plot. The ternary plot is an ingenious way of putting three dimensions onto a flat surface. I have found few good uses of this chart type, though.


Let's get to the core of the issue: the analyst started with 25 skills that are frequently required by data science and analytics jobs, and his goal is to classify these skills into three groups. The underlying method used to create these groups is factor analysis.

Each dot above is a skill. The HQ of each grouping of skills (known as a factor) is a corner of the plot. The closer the dot is to the corner, the more relevant that skill is to the skill group.

In the above chart, I highlighted four skills that are not clearly in one or another skill group. For example, Commuication straddles the Math/Stats and Business dimensions but scores lowly on the Technology/Programming dimension.


The ternary plot has a few problems. Like any scatter plot, once you have 10 or more dots, it is hard to fit all the data labels. Further, the axis labels must be carefully done to help readers understand the plot. 

Before long, the chart looks very cluttered. There just isn't enough room to get all your words in. Here is another version of the same chart -- wiht a different set of annotation.


Instead of drawing attention to those skills that have no clear home, this version of the chart focuses on the dots close to each corner.

In two cases, I classified two of the skills differently from the original. The Machine Learning skill is part of Math/Stats on my charts but it is part of Technology/Programming on the original.

The ternary plot is interesting and unusual but is only useful in selected problems.


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