The costs of higher education continue to rise as budgetary and financial pressures strain universities and its students. In particular, students in the sciences rely on computational software like Wolfram Research's Mathematica in their research and studies. The software and its associated syntax are highly useful tools and are necessary skills in the areas of engineering, mathematics, physics, and data science. However, the software is costly and is difficult for colleges and students to afford. With the advent of inexpensive credit card-sized computing devices like the Raspberry Pi and its partnership with Mathematica, the software can now be used at no cost. However, processing and speed are limited and performance is affected on a Raspberry Pi. Through the use of cluster computing, execution times of algorithms using Mathematica can be decreased while maintaining a lower cost than Mathematica's traditional licensing model. This research reports the design and configuration of a Raspberry Pi cluster for use with Mathematica in addition to the results of performance benchmark tests between algorithms executed on one node and four nodes. This work makes an important contribution to both information systems and science disciplines to decrease software licensing costs without sacrificing performance. Conveniently, this research project provided an opportunity for an undergraduate information systems major to learn and understand cluster computing in an experiential learning independent study project.