Gastroenterology and Hepatology (AMC), Amsterdam University Medical Center, Amsterdam, North Holland, The Netherlands.
ROA, Maastricht University, Maastricht, Limburg, The Netherlands.
PLoS One. 2024 Feb 29;19(2):e0299327. doi: 10.1371/journal.pone.0299327. eCollection 2024.
The growing demand for data scientists in both the global and Dutch labour markets has led to an increase in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skills of data scientists. This study addresses this issue by employing Correlated Topic Modeling (CTM) to analyse the content of 41 master programs offered by 11 Dutch universities and an interuniversity combined program. We assess the differences and similarities in the core skills taught by these programs, determine the subject-specific and general nature of the skills, and provide a comparison between the different types of universities offering these programs. Our analysis reveals that data processing, statistics, research, and ethics are the core competencies in Dutch data science and AI master programs. General universities tend to focus on research skills, while technical universities lean more towards IT and electronics skills. Broad-focussed data science and AI programs generally concentrate on data processing, information technology, electronics, and research, while subject-specific programs give priority to statistics and ethics. This research enhances the understanding of the diverse skills of Dutch data science graduates, providing valuable insights for employers, academic institutions, and prospective students.
全球和荷兰劳动力市场对数据科学家的需求不断增长,这导致大学提供的数据科学和人工智能 (AI) 硕士课程也有所增加。然而,关于数据科学家的具体技能仍缺乏明确性。本研究通过使用关联主题建模 (CTM) 来分析 11 所荷兰大学和一所校际联合项目的 41 个硕士课程的内容,以此来解决这个问题。我们评估了这些课程所教授的核心技能的差异和相似之处,确定了技能的特定学科和通用性,并对提供这些课程的不同类型的大学进行了比较。我们的分析表明,数据处理、统计学、研究和伦理学是荷兰数据科学和 AI 硕士课程的核心能力。综合大学倾向于注重研究技能,而技术大学则更倾向于 IT 和电子技能。广泛聚焦的数据科学和 AI 课程通常侧重于数据处理、信息技术、电子和研究,而特定学科的课程则优先考虑统计学和伦理学。这项研究增强了对荷兰数据科学毕业生多样化技能的理解,为雇主、学术机构和未来的学生提供了有价值的见解。