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Roundtable on Data Science Postsecondary Education A Compilation of Meeting Highlights (2020) / Chapter Skim
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2 Meeting #1: The Foundations of Data Science from Statistics, Computer Science, Mathematics, and Engineering
Pages 8-16

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From page 8...
... Roundtable members also examined foundations of data science from the fields of statistics, computer science, mathematics, and engineering and considered the needs of diverse data science communities. This Roundtable Highlights summarizes the presentations and discussions that took place during the meeting.
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From page 9...
... With proper training, statisticians offer a valuable contribution to data science because they can understand context, account for variability, design and analyze data, understand inference, foster reproducibility, work in multidisciplinary teams, and make data-driven decisions. Computer Science Charles Isbell, Georgia Institute of Technology The three educational pillars of computing are as follows: 1.
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From page 10...
... Census Bureau, noted that disciplinary jargon is problematic; if computer scientists adopted more accessible language, their literature would be more easily understandable to a greater number of people. Victoria Stodden, University of Illinois, Urbana-Champaign, focused on the importance of developing standards and best practices for software, while Mark Tygert, Facebook Artificial Intelligence Research, wondered about the role of programming in future curricula.
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From page 11...
... A mathe­ matical conceptualization of modern data science involves a blend of subfields in an integrative curriculum in which the varied mathematical tools are explained and jointly motivated. Moving forward, educators should consider how to evolve the mathematics curriculum to meet data science needs as well as how to better foster integrative teaching and learning.
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From page 12...
... , and Rebecca Nugent, Carnegie Mellon University, suggested that data science outreach efforts be directed toward humanities students. Hero mentioned that offering certificate programs tends to draw students from more diverse disciplines, but he also noted that student demand for data science courses is never an issue; what stifles enrollment is limited available faculty and course offerings.
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From page 13...
... Lida Beninson, National Academies, noted that for those who are hired for R1 positions, the average age at which that first happens is 42. Because of this, it is crucial to ensure that training programs for the next generation of researchers include highly transferable skills.
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From page 14...
... Ron Brachman, Cornell University, reiterated that data scientists are different from data engineers and that it is important to discuss varied career paths for students. Although everyone should be data literate, he does not see the value of having everyone enroll in data science programs.
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From page 15...
... Perry explained that the survey of his colleagues' interests was similar to those of Mishra: domain experts teach data-intensive courses focused on problems, not methods. Christopher Malone, Winona State University, asked whether the agencies hire people with undergraduate degrees in data science.
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From page 16...
... Malone cautioned of the dangers in combining computer science and statistics and calling it data science. He also suggested that the roundtable pay particular attention to smaller colleges in its future discussions about data science programs, as well as to the expectations for graduates.
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Key Terms

  • data scientists
  • science programs
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  • hiring organizations
  • biomedical data

  • data repositories
  • public data
  • science tools
  • science courses
  • science curriculum

  • science communities
  • undergraduate degrees
  • liberal arts
  • undergraduate level
  • machine learning

  • hire people
  • operations research
  • career paths
  • census bureau
  • data methodology

  • human data
  • rank data
  • graduate data
  • machine data
  • meet data

  • current data
  • hiring data
  • containing data
  • data analysts
  • designed data


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