  Understanding course enrollment patterns is valuable to predict upcoming
demands for future courses, and to provide student with realistic courses to
pursue given their current backgrounds. This study uses undergraduate student
enrollment data to form networks of courses where connections are based on
student co-enrollments. The course networks generated in this paper are based
on eight years of undergraduate course enrollment data from a large
metropolitan university. The networks are analyzed to identify "hub" courses
often taken with many other courses. Two notions of hubs are considered: one
focused on raw popularity across all students, and one focused on proportional
likelihoods of co-enrollment with other courses. A variety of network metrics
are calculated to evaluate the course networks. Academic departments and
high-level academic categories, such as Humanities vs STEM, are studied for
their influence over course groupings. The identification of hub courses has
practical applications, since it can help better predict the impact of changes
in course offerings and in course popularity, and in the case of
interdisciplinary hub courses, can be used to increase or decrease interest and
enrollments in specific academic departments and areas.

Social and Information Networks;Machine Learning