As data science (DS) grows in importance across disciplines, its integration into K-12 education remains fragmented, especially when the high school curricula are already packed. Embedding DS within existing computer science (CS) courses offers a practical solution by leveraging overlapping skills and content. To make the case for DS in CS classrooms, an important step is to examine how DS fits into the landscape of CS education through educational standards. This study explores the extent to which core high school DS topics align with the Computer Science Teachers Association (CSTA) standards, a widely adopted framework guiding K-12 CS education in the U.S. We conducted a qualitative document analysis comparing the high school CSTA standards to 61 DS subtopics synthesized from leading DS curricula and frameworks. Two researchers independently coded each pair of CSTA standard and DS subtopic for explicit or implicit connections, resolving discrepancies through consensus. To visualize alignment patterns, we developed an interactive Sankey diagram that maps the connection between the two frameworks. Our findings show that 16 of 17 DS topics align with at least one CSTA standard, primarily within the ''Data & Analysis'' and ''Algorithms & Programming'' concept areas. However, alignment often relies on broad or open-ended standards that flatten distinctions between DS practices at various level. Meanwhile, critical DS areas such as machine learning remain unaddressed. This analysis reveals both the promise and limitations of embedding DS in CS classrooms, informing future efforts to integrate DS meaningfully in K–12 education through standards-based curriculum design.

Data science education has gained momentum in recent years. Along with the development of curricula to teach data science, the number and diversity of tools for introducing data science to learners are also multiplying. The tools used to teach data science play a central role in shaping the learning experience. Therefore, it is important to carefully choose which tools to use to introduce learners to data science. This article presents a systematic analysis of 30 data science tools that are, or designed to be, used in introductory data science education for K-12 students. The identified tools list includes spreadsheets, visual analysis tools, and scripting environments. For each tool, we examine facets of its capabilities, interactions, educational support, and accessibility. For block-based programming tools, we also examine the data science functionalities available in that tool’s blocks. This paper advances our understanding of the current state of introductory data science environments and highlights opportunities for creating new tools to better prepare learners to navigate the data-rich world surrounding them.

This paper presents a pilot study of an interest-driven data science curriculum for high school students. The curriculum uses authentic and meaningful data exploration activities to situate data science in students' lived experiences. The curriculum aims to lay the computational foundation of data science and equip students with the necessary skills and practices to become informed and active citizens in our data-driven world. The pilot study, conducted in two sections of a computer science class, demonstrates the curriculum's inquiry-based approach, which allows students to formulate questions based on their interests and answer them by manipulating publicly available datasets. The study illustrates how a block-based learning environment and API data retrieval can be harnessed to support data science learning activities that situate the topics in learners’ lived experiences and create an engaging learning experience. The study advances our understanding of ways to use novel technologies to introduce learners to data science, emphasizing the practical implications of using authentic data and the inquiry-based approach in curriculum design.

Data is increasingly shaping the way people interact with each other and the world more broadly. For youth growing up in an increasingly data-driven society, it is critical they have foundational data literacy skills. A central component of data literacy is the ability to collect, analyze, visualize, and make meaning from data. All of these activities are mediated and shaped by the tools that youth use to carry out these data practices. Given the essential role tools play in enabling and supporting youth in engaging with and interpreting data, understanding what tools are used and how they are used in educational contexts will help us understand how youth are being prepared to be data-literate citizens. In this paper, we present the analysis of the data collection and analysis tools used in 4 widely adopted high school data science curricula. The analysis attends to both what tools are used as well as what datasets they are used to analyze. This work contributes to our understanding of the way youth are being introduced to concepts and practices from the field of data science and the role the tools play in shaping those experiences.