Ensuring the fairness of machine learning (ML) systems for individuals with disabilities is of paramount importance. To achieve this, proactive measures are required to identify and mitigate biases originating from data and models, thereby preventing potential harm or bias against people with disabilities. While previous research on ML fairness education primarily concentrated on gender and race fairness, the domain of disability fairness has received comparatively little attention. Addressing this gap, we adopted a student-centric approach to craft a disability fairness teaching intervention. We conducted a two-session focus group consisting of students experienced in machine learning, accessible computing, or both. The focus group outcomes underscored the significance of engagement and scaffolding strategies for effectively learning intricate topics. Consequently, we crafted a disability fairness hands-on programming assignment that delves into uncovering disability bias with a lens that takes intersectionality into account. The assignment was tailored for an introductory undergraduate data science (DS) course. We employed a combination of reflective questions and surveys to gauge the effectiveness of our approach. The findings indicate the success of our approach in promoting a deeper understanding of disability fairness within the context of DS education.