Process and Presentation

When we started planning our final project, we knew we wanted to explore something involving Carleton alumni and we wanted to map our findings with ArcGIS, and since we were one of a number of groups doing so, we had to find a caveat that would set our project apart from the others. For this, we chose to look closer into Carleton’s reputation for sending a large percentage of its graduates to graduate programs all over the country.

We had to start by finding data sets to use, and thankfully the Career Center and Office of Institutional Research and Assessment both had a number of publicly accessible data sets to choose from. We narrowed it down to a data set on the Top 50 institutions Carleton alumni have attended for graduate degrees since 1990 (a 2015 study, which breaks the total numbers for each institution down by degree type), and eight [8] data sets on popular majors at Carleton and where alumni of those majors have gone for graduate school and what types of careers they’re found themselves working in.

Since neither of these data sets included sensitive information (names, addresses, emails, spouse names, etc. — all data available to students and others with Carleton credentials in the Alumni Directory) we were able to move forward with no restrictions. Very little data cleaning needed to be done, so we used Excel to create csv files from the PDFs we downloaded, and used those csvs to make our visualizations in ArcGIS.

Our visualizations are five interactive maps that show the spread of Carleton alumni across the country (and in one case, technically across the world). The maps revealed different trends that we hadn’t at first expected, and they also solidified the fact that the U of M is by far the most popular graduate institution attended by prior Carls. The maps have interactive simple interactive features like scrollable pop-ups and collapsible legends, and one map showing the spread of majors even has a filter legend allowing you to view only certain majors on a map at a time.

In addition to these visualizations, we also created a number of bar charts that helped numerically visualize our data. Those charts can be found on the Data page.

Once we had compiled all our visualizations, it came time to create our Pecha Kucha presentation which we gave on Thursday, March 9. In our presentation, we gave the class a rundown of each of our visualizations and explained some similarities and differences that emerged through our research, and an embedded version of that presentation is below for your viewing: