Roundtable on Data Science Postsecondary Education A Compilation of Meeting Highlights (2020) / Chapter Skim
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3 Meeting #2: Examining the Intersection of Domain Expertise and Data Science
3 Meeting #2: Examining the Intersection of Domain Expertise and Data Science
Pages 17-30
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From page 17... ...
EMERGING NEEDS AND OPPORTUNITIES IN DATA-INTENSIVE DOMAINS English Ted Underwood, University of Illinois, Urbana-Champaign Underwood offered that there are both pedagogical opportunities for and challenges to integrating data science into an undergraduate English curriculum. Opportunities include the ability to explore nanswered u research questions about significant cultural patterns in works of literature, such as how and why descriptions of different parts of the world 17
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However, it is rare for undergraduate English majors to have any exposure to quantitative coursework, and many do not understand the value of applying data science methods across disciplines. Digital humanities courses are surfacing on some campuses, but they typically prioritize digital media over computational methods and quantitative reasoning.
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The discovery of the Higgs boson in 2012 and the direct detection of gravitational waves in 2016 demonstrate the value of combining domain expertise with methodological expertise to solve a data-driven problem arising from a large-scale physics experiment. In both instances, the use of novel hardware, computational infrastructure, and statistical methods was complemented by a team of diverse researchers asking the right questions and interpreting the results carefully.
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20 FIGURE 3.1 Expecting postsecondary students to become domain experts in astronomy while developing computer science, statistics, and programming skills presents a challenge. SOURCE: Joshua Bloom, University of California, Berkeley, presentation to the roundtable.
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To identify patterns or anomalies in texts that may affect government policy, historians could rely on machine learning approaches. Because the data wrangling involved in such work is labor-intensive, Ph.D.
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And Nicholas H orton, Amherst College, later commented that data science tools are now much simpler and cheaper for a wider variety of users to manipulate. Ullman worried about prescribing a specific data science program to first-year students who have not yet selected a major and would benefit from a broader introduction to the field.
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Choudhary suggested reevaluating general education curricula: Could foundational concepts of data science be integrated into general mathematics and science courses instead of creating new, separate courses? Underwood agreed that there are implications for the future of the general education curriculum, which traditionally has as its mission to equip students with diverse skills and tools.
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The goals of the MOOC, in particular, are to capitalize on students' interest in data science by exposing them to real problems; to strengthen delivering education at scale; to condense multiple courses into one introductory course; and to highlight the importance of database concepts in the broader data science discussion. This 8-week course includes instruction in the data science landscape, data manipulation at scale, analytics, visualization, and special applications.
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FIGURE 3.2 The eScience Institute's data science cycle includes education and training as a means to connect domain science inquiries to methodological developments. SOURCE: Produced by Ed Lazowska, University of Washington, and Moore/Sloan Data 25 Science Environments and presented by Bill Howe, University of Washington, to the roundtable.
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Data Science and Society. Concurrently, it plans to develop learning modules, increase advising support, and begin a topic review process for these courses.
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University of California, Berkeley Cathryn Carson, University of California, Berkeley Carson recounted that the University of California, Berkeley, strives to enable all students to "engage capably and critically with data" in response to increased student demand for data science training and increased diversity in faculty expertise. In an effort to achieve this goal, the university offers a foundational data science course, Data 8, (data8.org)
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The University of California, Berkeley, currently offers a short course for faculty to learn more about data science pedagogy and practice, and a number of course modules came directly from this work. There is also a student team working on data science education curriculum development, outreach and diversity, and program infrastructure.
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Underwood agreed, adding that the hub and connector model at the University of California, Berkeley, provides an appealing gateway to increase the visibility of data science among humanities students. Stodden also expressed concern about a shortage of professors if the demand for data science courses continues to increase, but domainbased courses could alleviate this strain on faculty.
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need to be fasttracked to address the challenges that data science curricula present. Carson noted the value of studying the many online data science courses that are already available before revising traditional undergraduate curricula.
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