An Analysis of Long-Term Financial Feasibility of Higher Education

This project explores the long-term financial feasibility of higher education. With rising costs of higher education and so many choices surrounding a degree such as degree type, sector of institution one attends, student loans one takes out, and field of study, it can be hard to discover which path will be most profitable long-term. This project analyzes data from the National Center of Education Statistics to see if there are existing relationships between these variables that contribute to different experiences in higher education and financial outcomes, specifically relating to future income and student loan payments. To do this I use various statistical tools and models such as multiple linear regression, tests for correlation, Kmeans clustering, and ANOVA testing. While most of these tests showed little or no relationship or significance, through clustering I found that those who get both an associate’s and a bachelor’s in the same field, make on average significantly more than those who get an associate’s and bachelor’s degrees in different fields. I also start the creation of a summary statistics interface with the intent to display data in a way that those with minimal scientific background could understand in the hopes that this project will continue to spark conversations around the inaccessibility of data surrounding higher education and the realistic outcomes that different paths through higher education will provide