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Professional Skills and Soft Skills in Data Scienceofessional skills (Sect. .) and soft skills (Sect. .). Professional skills are specific skills that are needed in order to engage in data science, while soft skills are more general skills that acquire unique importance in the context of data science. In each section, we address both cognitive, orga

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Machine Learning Algorithmsyze them from a pedagogical perspective. The algorithms we discuss are the K-nearest neighbors (KNN) (Sect. .), decision trees (Sect. .), Perceptron (Sect. .), linear regression (Sect. .), logistic regression (Sect. .), and neural networks (Sect. .). Finally, we discuss interrelations between the in

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https://doi.org/10.1007/978-3-662-04698-2a science programs for a variety of learners from a variety of disciplines (data science, computer science, statistics, engineering, life science, social science and humanities) and a variety of levels (from school children to academia and industry). While significant efforts are being invested in t

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September-November: the Approach of War,gle, consensus definition for data science is its multifaceted nature: it can be described as a science, as a research method, as a discipline, as a workflow, or as a profession. One single definition just cannot capture this diverse essence of data science. In this chapter, we first take an interdi

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Outward Remittances from the Gulf,al-world context (Sect. .), teaching STEM with real-world data (Sect. .), bridging gender gaps in STEM education (Sect. .), teaching twenty-first century skills (Sect. .), interdisciplinary pedagogy (Sect. .), and professional development for teachers (Sect. .). We conclude with an interdisciplinary