With the recent development of petascale computing systems, a long-term effort is needed to educate and train the next generation of researchers. As part of its graduate education component, the Virtual School of Computational Science and Engineering held a summer school in August 2008 entitled "Accelerators for Science and Engineering Applications," providing participants with knowledge and hands-on experience with graphics processing units (GPUs). In this paper, we present visualizations exploring the broad spectrum of summer school applicants and participants. We examine demographic information of the overall applicant pool, accepted and attending applicants, and remote participants, as well as apply hierarchical clustering and rule association techniques to all applicant data. These statistical and data mining analyses demonstrate the wide range of fields of study where research applications can be readily accelerated through the use of massively parallel computing resources.