This paper presents our approach to designing a General Education (GE) Mathematics/Quantitative Reasoning course launched in Fall 2022 to equip non-CS students with Python programming skills relevant to their disciplines. The course addresses the growing need for foundational programming knowledge and pathways to a diverse set of careers, including Data Science (DS), Artificial Intelligence (AI), and other computing-related areas. Underrepresented groups face barriers to DS and AI education due to economic, educational, and cultural disparities, making inclusive learning essential. Despite attracting a diverse student base, equity gaps persist, with higher DFW (Drop, Fail, Withdrawal) rates among minority students. This paper presents insights from pre- and post-course student surveys that examine students’ motivations, challenges, and overall perceptions of the course while analyzing student demographics and success rates between Fall 2022 and Fall 2024, to identify disparities between underrepresented minority (URM) students and other groups. Additionally, we provide an in-depth look at the course structure and how it addresses gaps in DS education for non-computing majors. Beyond our institution, fostering computing literacy across disciplines is essential, particularly in universities without GE requirements, such as outside of North America. Embedding programming modules into non-CS courses is an effective way to foster computing literacy, particularly in universities without GE requirements. We share strategies for seamless integration and propose targeted interventions for equity gaps, including adjusting course content and pacing, providing extra instructional support, and enhancing feedback. Our goal is to improve outcomes and promote equitable DS and AI education.