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Exploring Interdisciplinary Data Science Education for Undergraduates: Preliminary Results

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Diversity, Divergence, Dialogue (iConference 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12645))

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Abstract

This paper reports a systematic literature review on undergraduate data science education followed by semi-structured interviews with two frontier data science educators. Through analyzing the hosting departments, design principles, curriculum objectives, and curriculum design of existing programs, our findings reveal that (1) the data science field is inherently interdisciplinary and requires joint collaborations between various departments. Multi-department administration was one of the solutions to offer interdisciplinary training, but some problems have also been identified in its practical implementation; (2) data science education should emphasize hands-on practice and experiential learning opportunities to prepare students for data analysis and problem-solving in real-world contexts; and (3) although the importance of comprehensive coverage of various disciplines in data science curricula is widely acknowledged, how to achieve an effective balance between various disciplines and how to effectively integrate domain knowledge into the curriculum still remain open questions. Findings of this study can provide insights for the design and development of emerging undergraduate data science programs.

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Acknowledgments

This study is supported by a Teaching Development Grant sponsored by the University of Hong Kong and a grant (No. 61703357) by National Natural Science Foundation National Natural Science Foundation of China.

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Authors and Affiliations

  1. University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong S.A.R.

    Fanjie Li & Zhiping Xiao

  2. University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China

    Jeremy Tzi Dong Ng & Xiao Hu

Authors
  1. Fanjie Li
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  2. Zhiping Xiao
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  3. Jeremy Tzi Dong Ng
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  4. Xiao Hu
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Corresponding author

Correspondence to Xiao Hu .

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Editors and Affiliations

  1. Humboldt University of Berlin, Berlin, Germany

    Katharina Toeppe

  2. Renmin University of China, Beijing, China

    Hui Yan

  3. The University of Hong Kong, Hong Kong, Hong Kong

    Samuel Kai Wah Chu

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Li, F., Xiao, Z., Ng, J.T.D., Hu, X. (2021). Exploring Interdisciplinary Data Science Education for Undergraduates: Preliminary Results. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12645. Springer, Cham. https://doi.org/10.1007/978-3-030-71292-1_43

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  • DOI: https://doi.org/10.1007/978-3-030-71292-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71291-4

  • Online ISBN: 978-3-030-71292-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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