Data and computational literacies empower youth to be active participants and future leaders in our increasingly data-driven society. We conducted a design-based research project in which a small group (n = 5) of high school youth from diverse backgrounds learned how to code and create data visualizations and stories with public data about climate change in a 5-day (20 h total) free virtual summer program. Using interaction analysis methods to microanalyze students' engagements with data technologies, we developed the computational data literacy model (CDLM) to describe students' participation in various "computational data literacies" that emerged from our analysis (remixing, wayfinding, interpreting variables, making hypotheses, and personalizing data) and their use of different data tools (the code, data visualization, variable of interest, and story) to support scientific inquiry and reasoning. Using the CDLM, the presented analysis investigates how students navigated across coding and storytelling cycles of activities. Within those cycles, students collaboratively problem-solved in the code and engaged in collaborative inquiry, drawing on personal experiences to make multivariate hypotheses and stories about human impacts on carbon emissions. Our findings suggest that using a socioscientific issue (SSI) context supported students' back-and-forth movement between coding and storytelling activities, perhaps by affording greater personalization of the data, which, in turn, facilitated data-based reasoning. The findings of this study inform our understanding of the challenges and learning opportunities in this computational, data-rich intervention situated in socioscientific inquiry. We discuss future uses of our model for learning designs to support computational data activities about SSIs.