Data science teaching and learning models: focus on the Information Science area




data science, teaching and learning, curricula models


The Data Science field has become a focus of interest for teaching and research in the Information Science area in the last decade. However, due to the specificity of the area, there are still significant challenges when it comes to building curricula for undergraduate and graduate disciplines. What is observed is that students in the area have learning difficulties when taught by traditional approaches from the hard sciences. One of the most important international scientific journals in Information Science, the Journal of the American Society for Information Science and Technology (JASIST), opened a call for articles for a special issue of the journal with a focus on discussing the specificities of Data Science in its relations with Information Science. The call made available an extensive specialized bibliography on the subject. Nine works stand out in this research, directly indicative or referenced from the works indicated that are focused on the issue of teaching Data Science for the area. The analysis of the results points to a specific initiative to form a Data Science curriculum for the Information Science area, the School Data Science Curriculum Committee (iDSCC) initiative, and seven other curriculum models that could be adapted.


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Bishop, B., Allard, S., Benedict, K., Greenberg, J., Hoebelheinrich, N., Lin, X., & Wilson, B. (2019, September 24-26). Curricula models and resources along the data continuum: Lessons learned in the development and delivery of research data management and data science education. [Conference panel]. ALISE 2019, Knoxville, Tennessee, U.S.A.

Hagen, L., Zamir, M., Andrews, J., & Hamerly, D. (2019, March 31 – April 3). Undergraduate data science education in iSchools: Current practices and future directions. [Conference panel presentation]. iConference 2019, Washington DC, United States.

Hagen, L., M. Abdul-Muhammad, M., Andrews, J., Zamir, H., Hamerly, D., & Clifford-Bova, S. (2020, March 23–26). Undergraduate data science education in iSchools: Optics and politics [Conference panel presentation]. iConference 2020.

Oh, S., Song, I.-Y., Mostafa, J., Zhang, Y., & Wu, D. (2019, October 19-23). Data science education in the iSchool context [Conference presentation]. 82nd Annual Meeting, ASIS&T 2019, Melbourne, Australia.

Shah, C. Anderson, T., Hagen, L., & Zhang, Y. (2021). An iSchool approach to data science: Human-centered, socially responsible, and context-driven – A position paper. Journal of the Association for Information Science and Technology.

Virkus, S. & Garoufallou, E. (2019). Data science from a library and information science perspective. Data Technologies and Applications, 53:4, 422-441.

Virkus, S. & Garoufallou, E. (2020). Data science and its relationship to library and information science: a content analysis. Data Technologies and Applications, 54(5), 643-663.

Wang, L. (2018), Twinning data science with information science in schools of library and information science, Journal of Documentation, 74(6), 1243-1257.



How to Cite

Lopes Martins, D. (2022). Data science teaching and learning models: focus on the Information Science area. Advanced Notes in Information Science, 2, 140-148.