Although an increasing number of ethical data science and AI courses is available, pedagogical approaches used in these courses rely exclusively on texts rather than on algorithmic development or data analysis. Technical students often consider these courses unimportant and a distraction from the “real” material. To develop instructional materials and methodologies that are thoughtful and engaging, it is important to strike a balance: between texts and coding, between critique and solution, and between cutting-edge research and practical applicability. Finding such balance is difficult in the nascent field of responsible data science (RDS), where we are only starting to understand how to interface between the intrinsically different methodologies of engineering and social sciences. In this talk, Julia Stoyanovich will discuss responsible data science courses that she has been developing and teaching to technical students at New York University since 2019. These courses tackle a breadth of issues, including ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, and privacy and data protection. All course materials are publicly available at https://dataresponsibly.github.io/courses. Stoyanovich will also speak to some ongoing work on teaching responsible data science to members of the public in a peer learning setting, and will feature a comic book series on responsible data science that is targeted at current and future practitioners.