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Envisioning the Data Science Discipline The Undergraduate Perspective: Interim Report (2018) / Chapter Skim
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Pages 1-4

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From page 1...
... Teaching commonly used current methods will prepare them for the workplace, as will exposure to working in teams. Critical curricular topics include mathematical foundations, computational thinking, statistical thinking, principles of effective data management, techniques for data description and curation, data modeling approaches, effective communication skills, reproducibility challenges and current best practices, exposure to ethical dilemmas and problem-solving skills, and a range of domain-specific topics.
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From page 2...
... What follows in this interim report are initial observations and findings concerning the state of data science education, a discussion of forward-looking opportunities, and key questions on which the committee seeks broad input. The preliminary findings from the committee are described throughout this interim report and recapped in Chapter 5 where they are accompanied by a set of open questions developed by the committee about the future of data science education (also summarized in Box S.2)
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From page 3...
... BOX S.2 Questions for Public Input The committee has identified the following themes, which are discussed throughout this interim report, and is soliciting input on the open questions. Please visit the following webpage to provide input: http://www.nas.edu/EnvisioningDS.
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From page 4...
... ● What aspects of data science education are appropriate and feasible to develop at 2-year institutions? Assessment and Evaluation ● What evaluation and assessment objectives are currently being used in data science programs, and how will these differ in the future?
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Key Terms

  • science education
  • science programs
  • science curriculum
  • effective data
  • future data

  • data acumen
  • communication skills
  • pedagogical approaches
  • ethical dilemmas
  • skill sets

  • multiple disciplines
  • providing data
  • inform data
  • data scientists
  • data modeling

  • move data
  • building data
  • data description
  • encourage data
  • enables data

  • involve data
  • science encompasses
  • science educational
  • science approaches
  • key steps

  • delivering education
  • analytical approaches
  • seeks broad
  • include findings
  • undergraduate perspective


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