As part of a project with UC Berkeley UrsaTech, a club on campus that offers pro-bono tech consulting services to university-affiliated organizations, I launched a study investigating the socioeconomic factors influencing student development and the racial achievement gap due to the economic effects and racial disparities of the COVID-19 pandemic. The work was done in Python using data analysis libraries such as Pandas, Numpy, Matplotlib, Sklearn, Seaborn, Tensorflow, and Keras. Datasets from the National Center for Educational Statistics High School Longitudinal Study and Bureau of Labor Statistics Current Population Survey were cleaned, analyzed, visualized, and modeled. The findings were then compiled into a formal research report and published on Towards Data Science, where it was featured in the Editors' Picks and Data for Change columns. The full Github repository will all source code and relevant documentation can be found here.