关键词: 数据科学, 数据科学学位, 图书馆与信息科学, iSchools高校, 数据素养

Abstract:

[Purpose/Significance] The confluence of digital transformation and the Fourth Industrial Revolution has driven the emergence of data science as an interdisciplinary field. Data science leverages structured and unstructured data to discover knowledge and support decision-making, thereby reshaping research paradigms in information science, computer science, and the social sciences. This study focuses on the development of data science degree programs within member institutions of the iSchools Consortium, a global alliance of information schools. Through systematic empirical investigation, the study aims to unveil innovative features in the program's disciplinary positioning, curriculum architecture, and talent cultivation models. This research aims to inform global information science education institutions on how to optimize their disciplinary strategies and curricular designs for data science. Ultimately, this will address the challenges of knowledge system reconstruction and talent development iteration within the traditional library and information science (LIS) discipline amid its digital transformation. [Method/Process] This study employed a web-based survey and content analysis methodology to create a multi-dimensional analytical framework based on the 2023 iSchools Consortium membership directory. Using a stratified sampling approach that integrated disciplinary influence, as measured by the QS World University Rankings, and program maturity indicators, including curriculum comprehensiveness and industry partnership networks, eight representative U.S. higher education institutions were selected as core samples. A systematic empirical investigation was conducted to thoroughly analyze the current landscape of data science degree programs. The study focused on four critical dimensions: 1) degree-awarding structures such as degree types, concentration specializations, and accreditation standards; 2) credentialing ecosystems such as micro-credentials, stackable certificates, and non-degree pathways; 3) curricular architectures such as core course clusters, elective modules, and interdisciplinary integration mechanisms; and 4) career trajectory outcomes, such as sectoral distribution, occupational roles, and industry-specific skill premiums. [Results/Conclusions] The study summarizes the current state of data science discipline education in international iSchools from several perspectives, including the characteristics of degree program offerings, the reconstruction of disciplinary positioning, pathways for curriculum integration, and insights into employment trends. Based on this, it makes recommendations for developing China's domestic data science discipline. These recommendations include optimizing the disciplinary layout, innovating the curriculum system, and deepening industry-education integration. However, it should be noted that this research is constrained by its small sample size of eight institutions and its geographical scope, which is limited to the United States. In the future, the study could expand to encompass members of the European iSchools consortium, such as the iSchool at University College London and the iSchool at Humboldt University in Berlin, as well as emerging data science programs in the Asia-Pacific region. Through cross-national comparative analysis, it aims to reveal how culture, policies, and industrial ecosystems impact disciplinary development differently. Furthermore, the study could incorporate Learning Analytics technology to model learner behavior in data science courses offered on MOOC platforms, such as Coursera and edX. This would facilitate the refinement of course module granularity and adaptability to better meet learners' needs.Keywors: data science; data science degree; library and information science(LIS); iSchools; data literacy