I utilized a short version of the Integrated Postsecundary Education Data System (IPEDS) dataset obtained from a Kaggle Project to create a variety of visualizations.
In this first chart, I looked at the number of institutions, both public and private, per state.
Next, I looked at the number of public and private universities per region in the United States,
Next, I wanted to look at very specific data from NYU. I filtered the data to only include NYU and then created a new column with all the tuition and years available in the data set. I was then able to chart NYU's tuition amount by school year.
Next, I wanted to see the mean amount of tuition per year by geogphic region. By looking at the data, it is clear that the most expensive tuiton mean is in the New England geographic region of the United States.
I next looked at the percent of universities by region that offered doctorial programs. Interestingly, according to the data, the far west has the highest percent of universities that offer docteral programs.
Next, I looked at where these institutions were located (urban or rural).
I wanted to dive a little deeper into the data, so I broke out the type of degree with the enrollment for public and private institutions. This showed me that the trend of public universities enrolling more students than private universities generally did not change depending on degree type.
This violin plot helped me see the density of distribution of the values.
I also thought it would be interesting to visualize the enrollment data by geographic region.
I was also curious about the type of settings for different types of degrees so I created a heatmap to showcase that data.
Lastly, I created a radar map to compare total applicants, total admissions, total enrolled, undergraduate enrollment and graduate enrollment for public and private universities. This radar allowed me to compare the mean several different aspects of the data which was incredibly useful to visualize.