Teaching classification module in data science course for undergraduate non-STEM students using project based learning methodology
DOI:
https://doi.org/10.52041/iase2023.104Abstract
The current paper presents a detailed plan for teaching the classification module as one of the most important parts of the full data science course for non-STEM undergraduate students. Classification is one of the most common data analytics tasks. It is employed in myriad disciplines including marketing, finance, sociology, psychology, education, medicine, and other non-STEM areas. It is, therefore, appropriate to extend carry off with data mining methods to students who will deal with such problems during their professional careers. The overall data science course combines theory and practice and is taught in a "hands-on" format. The main assignment that is run throughout the course is a practical project, which simulates comprehensive research, starting from research questions, through data mining, to interpretation and decision-making. The contents and highlights may vary between different students' majors. We recommend addressing the teaching of the data science course as an interactive and dynamic process.References
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