Data Science Education in U.S. Informal Learning Environments: A Review of the Literature

This systematic review examines data science education in United States informal learning environments through analysis of 20 studies. Our analysis reveals the landscape of this emerging field. Our findings highlight three critical dimensions shaping informal DSE: the methodological and theoretical diversity of the field; the interplay of people, practices, and places; and emerging design principles and pedagogical approaches. Effective programs integrate technical skills with critical perspectives, connect to personally meaningful contexts, and position learners as knowledge producers rather than consumers. We identify several promising approaches, including critical data literacies, personal data exploration, data storytelling, embodied learning, and youth positioning as data agents. Yet, implementation challenges persist: literacy barriers, data complexity, equity gaps between intentions and practice, and limited assessment frameworks constrain the field’s ability to scale these innovations. Despite intentional efforts, notable gaps remain in rural, early childhood, and disability contexts. Critical approaches examining power and representation show promise for marginalized communities. Beyond technical recommendations, we argue for reconceptualizing data literacy toward collective sovereignty and assessment frameworks valuing transformative outcomes. Informal learning environments can serve not merely as preparation for existing data systems but as spaces for imagining and enacting more just alternatives that challenge power structures in data science education. Supplementary materials for this article are available online.