Data literacy is quickly becoming an essential topic for all students to learn in order to succeed in an increasingly data-driven world. Foundational data literacy skills currently live in a number of subjects across K-12 (e.g., data collection and analysis in science classes, statistical calculations in mathematics/statistics, data visualization and communication in civics/social studies), however, a growing number of schools and districts are introducing stand-alone data science (DS) courses. Given the centrality of computing and programming in the contemporary practice of DS, many of these courses include topics historically reserved for computer science (CS) classes. Further, many CS courses include dedicated time for DS topics (e.g., AP Computer Science Principles’ unit on Data). In many ways, DS educators and CS educators are working towards the same ends in complementary ways. However, at other times, the two disciplines are in tension, especially given the scarcity of time in K-12 student schedules for non-core subjects. This panel will explore what DS education and CS education can learn from each other, how each can contribute and advance the goals of the other, and how these two intertwined disciplines can productively live alongside each other in K-12 settings.