Teaching data science: an objective approach to curriculum validation

Abstract

Emerging careers in technology-focused fields such as data science coupled with necessary graduate outcomes mandate the need for a truly interdisciplinary pedagogical approach. However, the rapid pace of curriculum development in this field of inquiry has meant that curricula across universities has largely evolved in line with the internal disciplinary strengths of each institution rather than in response to the needs of graduates. To assist with the development of data science subjects the themes and content that contribute to each subject should be objectively validated. We propose the use of an objective test for data science curricula to quantify whether a particular degree programme maintains an interdisciplinary perspective unconstrained by single discipline bias. The test analyses a given curriculum and quantifies the subject components by category using natural language processing (NLP) techniques.