Documentation, Informaiton & Knowledge ›› 2023, Vol. 40 ›› Issue (2): 49-56.doi: 10.13366/j.dik.2023.02.049

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The iField Approach to Data Science Education:Interpretation and Reflections on the iDSCC Report

DONG Jing, XU Hao, WU Dan   

  • Online:2023-03-10 Published:2023-05-09
  • Contact: Correspondence should be addressed to WU Dan, Email:woodan@whu.edu.cn, ORCID:0000-0002-2611-7317
  • Supported by:
    This is an outcome of the project "Research on the Cultivation of Graduate Students' Academic Innovation Ability with an International Perspective under the Complex International Situation "(2020033)supported by the College Teaching Program of Hubei Province in 2020.

Abstract: [Purpose/Significance] Under the era of big data ,the demand for data science talents has attracted different disciplines to participate in data science education, and also triggered reflections on the nature characteristics of data science education in each discipline. The report "Data Science Curriculum in iField"(iDSCC Report)released by the iSchools Data Science Curriculum Committee gives the viewpoints of iField. [Design/Methodology] This study systematically combs the research methods and findings of the iDSCC Report, and analyzes the main conclusions and opinions. [Findings/Conclusion] The iDSCC Report concluded that the core characteristics of iField data science education include transdisciplinary, human-centered, forward-looking qualities, and its attention to how DS is taught; the core competencies of data science talents are reflected in human-centered thinking and data literacy; undergraduate and graduate education preservesome differences at the hierarchy, while keeping common foundation; and iField data science education will face several challenges in future development. [Originality/Value] Inspired by the iDSCC Report, this paper proposes that the domestic field of Information resources management should develop an iField data science education path with Chinese characteristics based on the Chinese context, keeping the original intention of Library and Information Science, combining the approaches of curriculum ideology and politics and university-industry collaborative education.

Keywords: Data science education, iField, Information resources management, Talent cultivation, Human-centered, Data literacy