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Interdisciplinary data science education

Full APA Reference

Stanton, J., Palmer, C. L., Blake, C., & Allard, S. (2013). Interdisciplinary data science education. In N. Xiao & McEwen (Eds.), Special Issues in Data Management (pp. 97–113). Washington, D.C.: American Chemical Society.

Publication Abstract

Data scientists are information professionals who contribute to the collection, cleaning, transformation, analysis, visualization, and curation of large, heterogeneous data sets. Although some conceptions of data science focus primarily on analytical methods, data scientists must also have a deep understanding of how project data were collected, preprocessed and transformed. These processes strongly influence the analytical methods that can be applied, and more importantly how the results of those methods should be interpreted. In the present chapter we provide background information on educational challenges for data scientists and report on the results of a workshop where experts from the information field brainstormed on the educational dimensions of data science. Results of the workshop showed that data scientists must possess a breadth of expertise across three areas – curation, analytics, and cyber-infrastructure – with deep knowledge in at least one of these areas. Workshop participants also underscored the importance of domain knowledge to the success of the data science role. Additionally, the workshop highlighted a factor that differentiates data science from other professional specialties: the emphasis on serving the data needs of information users and decision makers.