Introduction:
This work provides a comprehensive overview of the methodologies and key findings from the project "Data Skills Literacy for Educators," conducted during a fellowship at the Leeds Institute for Teaching Excellence (LITE). The project aimed to explore how educators can effectively learn and integrate data science (DS) and artificial intelligence (AI) skills into their teaching practices to enhance students' data literacy. Driven by the growing demand for data-literate graduates and professionals across various sectors, the project sought to understand the perspectives of DS and AI professionals and students, identifying gaps and opportunities for effective teaching practices. The primary beneficiaries of this research include educators, students, and policymakers, with a focus on preparing a workforce equipped for a data-driven world. This research is particularly timely given the rapid adoption of DS and AI technologies in both education and industry. The project was funded by the Leeds Institute for Data Analytics (LIDA).

Objectives:
The objectives of the project were threefold. First, it aimed to identify current gaps in DS and AI education by consulting with professionals and students. Second, it sought to develop actionable strategies for educators to enhance their data literacy and incorporate it into their teaching. Finally, the project aimed to establish a collaborative community for sharing DS and AI knowledge.

Methods:
To achieve these objectives, the project employed a mixed-methods research approach. The data collected were analyzed using a combination of thematic analysis and statistical methods to derive actionable recommendations. Surveys were conducted among DS and AI professionals and students to gather both quantitative and qualitative insights into their experiences and needs. Additionally, focus groups were organized to facilitate in-depth discussions and contextualize the survey findings. Regular meetings and workshops were held with the local DS and AI interest group, in collaboration with the Alan Turing Institute, to refine research questions and disseminate findings to broader communities.

Key findings:
The key findings of the project revealed several important insights. Students often perceive DS and AI as highly technical and inaccessible, underscoring the need for more approachable teaching methods. Professionals highlighted the importance of foundational data literacy skills over advanced technical expertise. There is a notable disconnect between current educational offerings and real-world industry needs. Collaborative, hands-on learning experiences were found to be highly effective for teaching DS and AI. Furthermore, educators expressed a need for tailored resources and training to confidently integrate DS and AI into their curricula.

Implications for practice:
The implications for practice are significant. Educators can adopt foundational, real-world examples to demystify DS and AI concepts for students. Institutions should prioritize professional development for educators in DS and AI. Curriculum development must align more closely with industry requirements. Creating interdisciplinary projects can foster practical applications of DS and AI skills. Policymakers can use these findings to advocate for systemic support in integrating DS and AI into education.

Keywords: Data Science, Artificial Intelligence, Education, Literacy.