Studying student course withdrawal data can provide important insights into factors impacting student success. However, institutions rarely analyze the student-reported reasons that accompany withdrawal requests. In this work, we analyzed 114 electronic withdrawal requests for CS301 (Data Structures & Algorithms) at California State University, East Bay, during Fall 2020–Spring 2025. Open coding and statistical tests were applied and correlations between reasons and GPA were examined. Time management was the most cited reason for withdrawal, followed by inadequate preparation, personal/family issues, and work. This analysis provides actionable steps Computer Science departments can implement, including improving data collection practices, targeted student advising, and more flexible course scheduling. These recommendations are particularly relevant for STEM programs serving similar highly diverse student populations.