The emerging field of Data Science has had a large impact on science and society. This has led to over a decade of calls to establish a corresponding field of Data Science Education. There is still a need, however, to more deeply conceptualize what a field of Data Science Education might entail in terms of scope, responsibility, and execution. This special issue explores how one distinguishing feature of Data Science--its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded--suggests serious implications for learning and education. The learning sciences is uniquely positioned to investigate how such contextual embeddings impact learners' engagement with data including conceptual, experiential, communal, racialized, spatial, and political dimensions. This special issue demonstrates the richly layered relationships learners build with data and reveals them to be not merely utilitarian mechanisms for learning about data, but a critical part of navigating data as social text and understanding Data Science as a discipline. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, agentive and socially aware Data Science Education.