IDEAS home Printed from https://ideas.repec.org/a/bla/jinfst/v74y2023i6p641-662.html
   My bibliography  Save this article

Data science curriculum in the iField

Author

Listed:
  • Yin Zhang
  • Dan Wu
  • Loni Hagen
  • Il‐Yeol Song
  • Javed Mostafa
  • Sam Oh
  • Theresa Anderson
  • Chirag Shah
  • Bradley Wade Bishop
  • Frank Hopfgartner
  • Kai Eckert
  • Lisa Federer
  • Jeffrey S. Saltz

Abstract

Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate‐level and undergraduate‐level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

Suggested Citation

  • Yin Zhang & Dan Wu & Loni Hagen & Il‐Yeol Song & Javed Mostafa & Sam Oh & Theresa Anderson & Chirag Shah & Bradley Wade Bishop & Frank Hopfgartner & Kai Eckert & Lisa Federer & Jeffrey S. Saltz, 2023. "Data science curriculum in the iField," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 641-662, June.
    • Handle: RePEc:bla:jinfst:v:74:y:2023:i:6:p:641-662
    DOI: 10.1002/asi.24701
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.24701
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.24701?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Melissa R. Bowers & Jeffrey D. Camm & Goutam Chakraborty, 2018. "The Evolution of Analytics and Implications for Industry and Academic Programs," Interfaces, INFORMS, vol. 48(6), pages 487-499, November.
    2. Daniel Carter & Dan Sholler, 2016. "Data science on the ground: Hype, criticism, and everyday work," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(10), pages 2309-2319, October.
    3. Michael Buckland, 1999. "The landscape of information science: The American Society for Information Science at 62," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(11), pages 970-974.
    4. Shifra Baruchson‐Arbib & Jenny Bronstein, 2002. "A view to the future of the library and information science profession: A Delphi study," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(5), pages 397-408.
    5. Daphne R. Raban & Avishag Gordon, 2020. "The evolution of data science and big data research: A bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1563-1581, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deming Lin & Tianhui Gong & Wenbin Liu & Martin Meyer, 2020. "An entropy-based measure for the evolution of h index research," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2283-2298, December.
    2. Tuba Bircan & Almila Alkim Akdag Salah, 2022. "A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    3. João Reis, 2023. "Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python," Logistics, MDPI, vol. 7(4), pages 1-19, December.
    4. Wieslawa Gryncewicz & Monika Sitarska-Buba, 2021. "Leading Research by Institutions and Authors: A Modern Research Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 1012-1026.
    5. Heimeriks, Gaston & van den Besselaar, Peter & Frenken, Koen, 2008. "Digital disciplinary differences: An analysis of computer-mediated science and 'Mode 2' knowledge production," Research Policy, Elsevier, vol. 37(9), pages 1602-1615, October.
    6. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Haoran Zhu & Lei Lei, 2022. "The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis," SAGE Open, , vol. 12(2), pages 21582440221, April.
    8. Shalini R. Urs & Mohamed Minhaj, 2023. "Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 606-622, June.
    9. Kala C. Seal & Linda A. Leon & Zbigniew H. Przasnyski & Greg Lontok, 2020. "Delivering Business Analytics Competencies and Skills: A Supply Side Assessment," Interfaces, INFORMS, vol. 50(4), pages 239-254, July.
    10. Wallace Koehler, 2001. "Information science as "Little Science":The implications of a bibliometric analysis of theJournal of the American Society for Information Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 117-132, April.
    11. Cyprian. I. Ugwu & I. J. Ezema, 2018. "Planning for Knowledge Management Implementation in Academic Libraries: Empirical Evidence From Federal University Libraries in Nigeria," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-28, December.
    12. Fernando Garrigós-Simón & Silvia Sanz-Blas & Yeamduan Narangajavana & Daniela Buzova, 2021. "The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    13. Yin Zhang & Il‐Yeol Song & Theresa Anderson & Dan Wu, 2023. "About JASIST special issue on “Data Science in the iField”," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 601-605, June.
    14. Bhaskar Mukherjee, 2009. "Scholarly research in LIS open access electronic journals: A bibliometric study," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 167-194, July.
    15. Fred Fonseca, 2021. "Whether or when: The question on the use of theories in data science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(12), pages 1593-1604, December.
    16. Aaron Babier & Craig Fernandes & Ian Yihang Zhu, 2023. "Advising Student-Driven Analytics Projects: A Summary of Experiences and Lessons Learned," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 121-135, January.
    17. Mu-Hsuan Huang & Yu-Wei Chang, 2012. "A comparative study of interdisciplinary changes between information science and library science," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 789-803, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jinfst:v:74:y:2023:i:6:p:641-662. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.