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The evolving landscape of data science education poses challenges for instructors in general education classes. With the expansion of higher education dedicated to cultivating data scientists, integrating data science education into university curricula has become imperative. However, addressing diverse student backgrounds underscores the need for a systematic review of course content and design. This study systematically reviews 60 data science courses syllabi in general education across all universities in Taiwan. Utilizing content analysis, bibliometric, and text-mining methodologies, this study quantifies key metrics found within syllabi, including instructional materials, assessment techniques, learning objectives, and covered topics. The study highlights infrequent textbook sharing, with particular focus on Python programming. Assessment methods primarily involve participation, assignments, and projects. Analysis of Bloom’s Taxonomy suggests a focus on moderate complexity learning objectives. The topics covered prioritize big data competency, analytical techniques, programming competency, and teaching strategies in descending order. This study makes a valuable contribution to the current knowledge by tackling the challenge of delineating the specific content of data science. It also provides valuable references for potentially streamlining the integration of multiple disciplines within introductory courses while ensuring flexibility for students with varying programming and statistical proficiencies in the realm of data science education. |
