This report describes the experience of two instructors of the course “PIC 16A: Python Programming with Applications” at the University of California, Los Angeles. PIC 16A covers Python programming constructs and elementary data science and machine learning workflows. The design of the course is explicitly targeted toward reducing gender gaps in confidence and interest in computing and data science. Interventions implemented in the course include active learning, project-based assessment, and structured learning communities of practice. The two instructors administered entrance and exit surveys to over 150 students. While these surveys were initially intended to inform course improvement, we also analyze them here in order to study the effectiveness of the active, project-based course design in reducing gender gaps. Results indicate that students in these courses consistently enhanced their confidence related to programming and data science and their comfort navigating interactive course environments. Furthermore, these improvements were often more pronounced among female students. However, improvements in career orientation were less consistent across studied course sections. We contextualize our findings against differences between courses, including implementation mechanics, strike-driven disruptions, and instructor identity.