Over the past several years, has made policy changes that have led to a perceived upward trend of student success, retention, and performance in Computer Science (CS). In our initial attempts to utilize basic methods of statistical analysis to perform exploratory data analysis (EDA), we found it difficult to fully discern what the extent of the trend of student performance was. To remedy this, we developed a dashboard tool which we call CSEdD for the express purpose of performing EDA on student information. It was important for our analysis that we were able to understand how different collectives of students, such as those across various demographic groups such as first-generation students, students of different racial identities and ethnicities, and gender identities, to fully understand whether the changes implemented were equitable in nature. Anonymized data, breakdown with colorized figures, and supplemental materials are available on an OSF repository, here: https://osf.io/m7b4p/?view_only=c3731da49e1044338a002f6a9c29381b.