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Teaching Data Science the Interdisciplinary Way: Learning Cycles and Diverse Skillsets
Umeå University, Faculty of Social Sciences, Department of Informatics. (Swedish Center for Digital Innovation)
Umeå University, Faculty of Social Sciences, Department of Informatics. (Swedish Center for Digital Innovation)
2018 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Due to the potential to extract useful knowledge by means of data mining and statistical analysis, there has been a significant increase in education that teach data science courses, issues degree certificates, and offers master’s programs. Such programs focus on three skill sets: 1) information technology skills necessary for accessing and working with data (e.g. relational databases, OLAP); 2) analytical skills drawing from various disciplines that enable data analysis (e.g. statistical analysis, machine learning, econometrics); 3) business and communication skills that facilitate appropriate problem formulation and value extraction from data science solutions. Successful educational programs need to synthesize and balance these three aspects.However, recent reports show that academic programs consistently underperform in preparing data science professionals. We studied the challenges and opportunities of teaching data science skills to students of diverse backgrounds (often the case in Master’s level programs). Our preliminary findings suggest that to balance the three skill sets educators need to recognize their distinct learning cycles. A learning cycle consists of exploration, concept introduction, and application and the emphasis on each stage varies greatly across skill sets. Technical data science skills rely on a multitude of separate systems and technologies involving numerous concepts that require continuous exploration, short cycles of learning (and assessment). Analytical skills require deeper engagement, hence longer learning cycles with an emphasis on concept introduction and formal instruction. Business and communication skills rely on engagement with high-level social systems (e.g. an organization, a market) and require longer involvement with the learning context, almost exclusively achieved by simulation projects or projects with industry partners. Our study indicates that data science education must consider the distinct learning cycles students experience, and suggests methods for doing so.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Data science, Information Systems, Teaching, Learning Cycle, Analytics, data science education
National Category
Information Systems, Social aspects Pedagogy Information Systems
Research subject
Business Studies; Computer and Information Science; Education; Systems Analysis
Identifiers
URN: urn:nbn:se:umu:diva-153462OAI: oai:DiVA.org:umu-153462DiVA, id: diva2:1264931
Conference
The 6th Swedish Workshop on Data Science
Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2018-11-21

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Sandberg, Johan

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf