Fields of study that concern science, technology, engineering and mathematics, typically known as ‘STEM’ fields, exhibit a misrepresentation in their employee demographic breakdown. This is especially true of physics and astrophysics, where minorities from lower income areas have an extremely low chance of fostering an interest, much less a career, in physics due, in part, to the lack of access to advanced computing and scientific technology. This means a massive amount of skill and talent is being lost in the American physics workforce. NRAO, the National Radio Astronomy Observatory, is one institution that feels the effects of this loss of talent, especially with projects on the horizon that will create a massive influx of data like the Next Generation of the Very Large Array. The goal of the project carried out by NRAO NINE’s summer students was to prototype a more accessible way for astrophysics curriculum to be created and distributed to lower income minorities. A set of accessiblelessons was chosen that could take a student with little to no knowledge to doing analysis of astronomical data, specifically obtained throughsky surveys currently underway with the Very Large Array. These lessonswere created and distributed using industry standard tools like Jupyter and git. We found that the most accessible way to create a curriculum was to make it engaging, interactive, and provide use outside the context of the specific lessons, which was made possible by markdown integration in Jupyter notebooks. The uses and implications ofserver-side computing for a class of students was also revealed through the development of this prototype.