Abstract: This study computes the correlation of student grades between pairs of courses in a large university. Course network graphs are then generated, where courses are represented as nodes and courses are connected if they have a high degree of grade correlation. Graph mining and network analysis tools visualize the course networks, identify course clusters and course cliques, and compute informative network statistics. Results are analyzed for pairs of courses and courses grouped by academic department or program of study. Strong course similarity groupings are observed within scientific disciplines, between pre-health courses, and within subfields of computer science. No prior study using this notion of course similarity has been conducted.