Using the Datasquad model to join practical "people skills" with data science education
DOI:
https://doi.org/10.52041/iase24.603Abstract
With the extremely rapid developments in the field, teaching data science in a sustainable way is quite challenging. We know there are demands for skilled data scientists, but we also know there is a gap between what can be covered in the usual curriculum and the real world of data practitioners encounter in their post-college lives. The DataSquad model is empowering on several levels: for beginning students it provides a foothold to more robust data literacy through working with more advanced peers on documentation. For those with more technical experience, they can develop human skills through project management, peer mentoring, and problem solving. This paper outlines how the early success of the Carleton DataSquad can contribute to tackling the students’ challenge of needing experience before they can get a job.References
Connor, H., & MacFarlane, K. (2007). Work Related Learning (WRL) in HE – a scoping study. Centre for Research in Lifelong Learning, Glasgow, Caledonian University. https://s3.eu-west- 2.amazonaws.com/assets.creode.advancehe-document- manager/documents/hea/private/wrlreport_january2007_1568036946.pdf.
Dennis, T. (2021, May 20). Using Student Workers in the Delivery of Data Service [Video]. YouTube. https://www.youtube.com/watch?v=wQi2sFVKWrI.
D’Ignazio, C., & Klein, L.F. (2020) Data feminism. MIT Press.
Hauhart, R.C., & Grahe, J.E. (2015) Designing and teaching undergraduate capstone courses (Jossey- Bass higher and adult education series). John Wiley & Sons.
Howe, S. (2010). Where are we now? Statistics on capstone courses nationwide. Advances in Engineering Education, 2. 1-27.
Inamdar, S. N., & Roldan, M. (2013). The MBA capstone course: Building theoretical, practical, applied, and reflective skills. Journal of Management Education, 37(6), 747-770. https://doi.org/10.1177/1052562912474895.
Isuster, M., & Rod, A.B. (Eds.) (forthcoming), Data culture in academic libraries: A practical guide to building communities, partnerships, and collaborations. Association of College and Research Libraries.
Jackson, D. (2015) Employability skill development in work-integrated learning: Barriers and best practice, Studies in Higher Education, 40(2), 350-367, https://doi.org/10.1080/03075079.2013.842221
Kay, J., Ferns, S., Russell, L., Smith, J., & Winchester-Seeto, T. (2019). The emerging future: Innovative models of work-integrated learning. International Journal of Work-Integrated Learning, Special Issue, 20(4), 401-413. https://www.ijwil.org/files/IJWIL_20_4_401_413.pdf.
Lackie, P. (2021a, May 20). Professional Development and the delivery of Data Support Services with Student labor [Video]. YouTube. https://www.youtube.com/watch?v=qCyihGcnbKk.
Lackie, P. (2021b, May 20). Managing Students in the delivery of Data Support Services [Video]. YouTube. https://www.youtube.com/watch?v=xrq_yWMOQhY.
Lackie, P (2019, May 31). Responding to Data Support Challenges with a Student DataSquad [Video]. IASSIST. https://doi.org/10.5281/zenodo.3620820.
Lasrado, F., Dean, B. A., & Eady, M. J. (2023). Inclusive work-integrated learning in higher education: a scoping review. Studies in Higher Education, 49(9), 1588–1609. https://doi.org/10.1080/03075079.2023.2271048.
Lawlis, T., Mawer, T., Andrew, L., & Bevitt, T. (2024). Challenges to delivering university health- based work-integrated learning to students with a disability: a scoping review, Higher Education Research & Development, 43(1), 149-165, https://doi.org/10.1080/07294360.2023.2228209.
McCormack, J., Beyerlein, S., Bracklin, P., Davis, D., Trevisan, M., Davis, H., Lebeau, S., Gerlick, R., Thompson, P., Khan, M.J., Leiffer, P. & Howe, S. (2011) Assessing Professional Skill Development in Capstone Design Courses. International Journal of Engineering Education, 27(6) 1308–1323.
Miller, S. (2014). Collaborative Approaches Needed to Close the Big Data Skills Gap. Journal of Organization Design, 3(1), 26–30. https://doi.org/10.7146/jod.9823.
Moriña, A., & Biagiotti, G. (2022). Inclusion at University, Transition to Employment and Employability of Graduates with Disabilities: A Systematic Review. International Journal of Educational Development, 93. https://doi.org/10.1016/j.ijedudev.2022.102647.
Stanton, W., & Stanton, A. (2020). Helping Business Students Acquire the Skills Needed for a Career in Analytics: A Comprehensive Industry Assessment of Entry‐Level Requirements. Decision Sciences Journal of Innovative Education, 18. https://doi.org/10.1111/dsji.12199.
Stwalley, R. (2024) Assessing Improvement and Professional Career Skills in Senior Capstone Design through Course Data. International Journal of Engineering Pedagogy, 7(3), 130-146. https://www.learntechlib.org/p/207438/.
Thompson, K., & Kellam, L. (2016). Databrarianship: The academic data librarian in theory and practice. Association of College and Research Libraries. https://openlibrary.org/books/OL27450574M/Databrarianship.
Vu, T., Ferns, S. & Ananthram, S. (2022) Challenges to international students in work-integrated learning: a scoping review, Higher Education Research & Development, 41(7), 2473-2489, https://doi.org/10.1080/07294360.2021.1996339.
Young, E., Wajcman, J. & Sprejer, L. (2023) Mind the gender gap: inequalities in the emergent professions of artificial intelligence (AI) and data science. New Technology, Work and Employment, 38, 391–414. https://doi.org/10.1111/ntwe.12278.
