Our first year biomedical engineering course exposes students to multiple engineering and design techniques within an overarching theme of understanding health inequity. Currently, the semester-long curriculum excludes computational methods such as Python programming and Machine Learning, which are usually not introduced until more advanced BME courses in the student's third and fourth year. This paper details the development and student feedback of a 2-week computational module in a team-based course focused on health inequity. Google Colaboratory was used to host the modules and provide a coding environment for students. The end-of-semester survey indicated that 53% of students (N=127) agreed they gained a basic understanding of Python and were comfortable with the programming language and that 72% of students agreed that the module helped them understand the potential of Machine Learning to aid in analyzing health data and identifying health inequities. Student feedback indicated strong interest in spending additional time on a more in-depth project. Nonetheless, we were able to provide students experience with Python programming and Machine Learning. Further, there was a statistically significant increase (34% to 88%, pvalue < 0.001) in students who ''agreed'' or ''strongly agreed'' that ''I can define and ideate solutions for issues in health inequity.'' This module enables first year students to use Python and Machine Learning to analyze health inequities and prepares them to apply computational methods in future courses. We have included links to all of the Python and Machine Learning guides, assignments, coding, and answer keys for educators to implement this module.

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