Visualizations are a tool for understanding data. They help to express relationships between data, the significance of data points, and the importance of data without having to scroll through tables or calculate percentages. Visualizations provide immediate understanding based on vision and interpretation of the image. All of the data used for this exercise is from the 2013 College ScoreCard dataset, which provides the most complete data I found as of this date.
One of the first images I created is a map of the United States showing the tuition of bachelor’s degree granting universities across geographies. The size of circles indicate the amount of in-state tuition charged by the institution, while the color of the circle indicates whether the institution is public (light blue) or non-profit private (dark blue). As seen, dark blue circles tend to be significantly larger on average as expected. The visualization also shows that in terms of distribution, more expensive private universities tend to be located in the Northeast while the South and Midwest feature less expensive public and private options.
The second graph I produced shows the geographic distribution of student populations using a bubble graph. The graph demonstrates that most undergraduate students are located in the Southeast region (1,994,386) while the fewest are located in the Rocky Mountains region (410,525). I created a legend separately to spell out each state.
My final visualization shows the correlation between Family Income and SAT at universities in Illinois. As seen in the graph, there appears to be a positive correlation between an increase in family income and the related increase in test scores. Northwestern University and the University of Chicago, two of the most prestigious universities in the state, both have the highest test scores and the highest average family income of any undergraduate degree granting institution.
While it’s important to note that these visualizations are not developed using entirely scientific methods, they still serve as a useful way to digest what is otherwise a noisy dataset or table. This exercise has taught me the importance of visualizations and the utility that tools such as Tableau can provide when dealing with data.