Adapting Computational Skills for AI Integration

In today's data-driven world, the importance of data literacy is paramount. However, software engineering education has not adequately addressed integrating comprehensive data science curricula, leaving students ill-equipped for the future of artificial intelligence (AI), which is built on the foundations of data science. This gap is exacerbated by the lack of tailored courses and the intimidating nature of existing tools for begin-ners. Consequently, students often miss out on essential skills like data cleanup, real-world application of machine learning (ML) algorithms, and the integration of big data in software products. This paper addresses these challenges by proposing a novel approach to applied data science for software engineering students. We argue for a shift from traditional algorithm-focused teaching to a curriculum emphasizing real-world problem-solving, lever-aging data science techniques. By empowering students to define and tackle their own data-driven projects, we aim to increase motivation, enhance data literacy, and instill a data-thinking mindset in future software engineers to prepare them for the AI world. Overall, this paper contributes to the advancement of software engineering education for young learners by offering a comprehensive framework, the data action educational framework (DAEF), and a data science toolkit that enables DAEF by empowering learners to create original data-driven mobile apps.