Introducing Data Science Concepts into STEM-Driven Computer Science Education

This chapter focuses on Data and Data Science (DS) due to their importance. The other reason is the coherence and relationship of DS concepts with the IoT. Data appears in three formats (structured, semi-structured, and unstructured). Currently, with the IoT technology, we are gradually entering into the era of ‘big data’. Before use, we need first to extract, collect, store, analyse, classify, and process data in various ways to be useful for multiple applications. The proposed methodology covers the full cycle of data processing (collecting, transferring, transforming, and analysing). The methodological contribution of this research includes: (i) A strong focus on modelling processes and explicit model creation using different modelling approaches. (ii) A strong adherence to the Big Data domain and solving real-world problems, thus enabling the enforcement of STEM-driven CS education in terms of modelling and contributing to engineering education. (iii) In addition, the proposed methodology ensures a flexible re-configuration of the previously developed SLE by adding new components (tools) regarding DS. From a pedagogical perspective, this approach (i) enables collaborative learning. (ii) It enhances computational thinking, scientific thinking skills, and data-driven skills. (iii) The approach contributes to increased motivation (all students have passed the full cycle of DS processes). (iv) The approach also supports personalised learning since students can work at their own pace and with relevant intensity at home. (v) The approach covers a set of pedagogical approaches, such as learning-by-doing, problem-based, and inquiry-based, by adding pedagogical value.