Envisioning the Data Science Discipline The Undergraduate Perspective: Interim Report (2018) / Chapter Skim
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2 Acquiring Data Science Skills and Knowledge
2 Acquiring Data Science Skills and Knowledge
Pages 10-18
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From page 10... ...
This chapter discusses some of the foundational, translational, ethical, and professional skills that make it possible for students to be effective data scientists. FOUNDATIONAL SKILLS What are the key ideas and principles to be included in the data science curriculum?
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From page 11... ...
Data acumen can be developed over time through research experience, industry partnerships, courses in creative data analysis, domain-specific data science courses or experiences, and extensions of capstone-like experiences throughout the curriculum. It can be enhanced through exposure to current key components of data science, including mathematical foundations, computational thinking, statistical thinking, data management, data description and curation, data modeling, ethical problem solving, communication and reproducibility, and domainspecific considerations.
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, and introductory courses designed to appeal to a wide student audience. High-impact educational practices, such as those put forth by the Association of American Colleges and Universities, describe teaching and learning practices that have been shown to be beneficial for postsecondary students from many backgrounds.
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From page 13... ...
. This aligns with the curriculum practices recommended by the American Statistical Association to teach students how to strengthen communication skills within the data science field (ASA, 2014)
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Capstones are offered both in departmental programs and, increasingly, in general education. For example, the Statistics Capstone Course at the University of Georgia provides students the opportunity to engage in a year-long data analytics project that enhances understanding of advanced statistical material while reinforcing oral and written communication skills (Lazar et al., 2012)
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From page 15... ...
In other words, educators would train students to do data science in real application contexts, incorporating real data, broad impact applications, and commonly deployed methods, as well as working in teams. Students benefit from experiences such as carrying out sentiment analysis of texts, generating interactive maps to explore spatial data, assessing relationships between links within social networks, 15
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Educational systems and structures need to prepare students to inhabit a world that will have different tools than those currently available. Students develop judgment through the practice of working through the entire data science cycle.
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An important lesson for students to learn is that transparency, trust-building, and validation/replication are key concepts; reputable data scientists are able to show why they do their work, explain the benefits that will emerge from it, and characterize and communicate the limitations of that work. The trade-offs related to privacy play a key role in this discussion as well.
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From page 18... ...
Finding 2.3: Incorporating ethics into an undergraduate data science program provides students with valuable skills that can be applied to complex, human-centered questions across disciplines. PROFESSIONAL SKILLS Broad professional skills are particularly critical in data science (BHEW, 2017; Hicks and Irizarry, 2017)
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