Data Visualization based on Edward R.Tufte in Python
This is an exercise from DataQuest's Data Science (Python) track . In this exercise, the goal is to use Edward Tufte's data story telling technique.
The original chart was:
In this chart the background ticks were removed.
To look at STEM degrees, the charts was converted into a 2x2 matrix. In this chart, the borders are removed to make the charts cleaner.
However, Tufte argues for connected charts with the "Men" and "Women" labels on the first and last charts. He also contends that the charts should be ordered from largest variation to smallest variation to make things easier to read and better understand the relationship between charts.
Thus , the final chart is:
Important notes:
The original chart was:
In this chart the background ticks were removed.
To look at STEM degrees, the charts was converted into a 2x2 matrix. In this chart, the borders are removed to make the charts cleaner.
However, Tufte argues for connected charts with the "Men" and "Women" labels on the first and last charts. He also contends that the charts should be ordered from largest variation to smallest variation to make things easier to read and better understand the relationship between charts.
Thus , the final chart is:
Important notes:
- The data is originally from the US Department of Education. The Statistics department releases a data set annually containing the percentage of bachelor's degrees granted to women from 1970 to 2012. The data set is broken up into 17 categories of degrees, with each column as a separate category.
- DataQuest dataset comes from Randal Olson. He is a data scientist at University of Pennsylvani. He cleaned the data set and made it available on his personal website.
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