Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis
通过分析全球32所iSchools的研究生数据科学课程，研究数据科学领域的演变，发现课程偏向数据可视化、机器学习等，而统计和eScience/研究数据管理内容较少。
Abstract Data Science (DS) has emerged from the shadows of its parents—statistics and computer science—into an independent field since its origin nearly six decades ago. Its evolution and education have taken many sharp turns. We present an impressionistic study of the evolution of DS anchored to Kuhn's four stages of paradigm shifts. First, we construct the landscape of DS based on curriculum analysis of the 32 iSchools across the world offering graduate‐level DS programs. Second, we paint the “field” as it emerges from the word frequency patterns, ranking, and clustering of course titles based on text mining. Third, we map the curriculum to the landscape of DS and project the same onto the Edison Data Science Framework (2017) and ACM Data Science Knowledge Areas (2021). Our study shows that the DS programs of iSchools align well with the field and correspond to the Knowledge Areas and skillsets. iSchool's DS curriculums exhibit a bias toward “data visualization” along with machine learning, data mining, natural language processing, and artificial intelligence; go light on statistics; slanted toward ontologies and health informatics; and surprisingly minimal thrust toward eScience/research data management, which we believe would add a distinctive iSchool flavor to the DS.