Data Science Education

Cao, Longbing

doi:10.1007/978-3-319-95092-1_11

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Despite the iField's rich potential, scholars seldom addressed ethics in the 2010s. Many publications neglected the matter altogether (Cao, 2018; Chen et al, 2019; Danyluk et al, 2019; Demchenko et al, 2016, 2017; Dumbill et al, 2013; Durr, 2020; Han & Zhu, 2017; Kim, 2016; Oh et al, 2019; Raj et al, 2019; Tang & Sae‐Lim, 2016; Varvel et al, 2012; Wiktorski et al, 2016). Others mentioned it only cursorily (Cao, 2019; Irizarry, 2020; Song & Zhu, 2017).…”

Section: Graduate Data Science Educationmentioning

confidence: 99%

Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice

Poole

2023

Proceedings of the Association for Information Science and Tech

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Data Science is a burgeoning area in the iField. But Data Science practices have far outstripped the field's ethical safeguards. We argue that Data Science graduate education programs must address this critical problem. In this theoretical and conceptual paper, we posit an ordinary macroethics that we call data flourishing. We contend that this macroethics is most appropriately developed through a holistic, human‐centered data science (HCDS)‐based pedagogy that concentrates on cultivating communities of ethical practice (COEPs) through social learning. We favor embedding this macroethics throughout iField programs' graduate data science curricula and by extension, the entire data science education enterprise. This paper aligns with the 2023 ASIS&T annual meeting theme of translating research into practice, particularly the subthemes of “improving decision‐making” and “understanding the power of information to develop human happiness, equality, and wellbeing.”

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Section: Graduate Data Science Educationmentioning

confidence: 99%

Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice

Poole

2023

Proceedings of the Association for Information Science and Tech

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“…Some institutions also offer an interdisciplinary DS and education program and the challenges of teaching DS with an interdisciplinary teaching team are also discussed (Bates et al, 2020; Wu, 2019). While many disciplines introduce DS curricula and the number of DS programs and courses is growing, it has been observed that they are mostly based on the re‐labeling and combination of existing curricula from statistics, computing and informatics, business, and information technology curricula (Cao, 2018b). For example, the field of business and economics tends to combine DS education with business applications and consideration of business processes (Miah et al, 2020), and the field of statistics tends to address why and how to set up DS programs under the discipline of statistics (Aerts et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

“…In response to this need, many disciplines have begun developing data‐related programs (Wu, 2019). Despite the growing number of DS programs and courses, however, most such programs have been based on existing curricula from statistics, computing and informatics, business, and information technology programs (Cao, 2018b).…”

Section: Introductionmentioning

confidence: 99%

Data science curriculum in the iField

Zhang

Hagen

et al. 2022

Asso for Info Science & Tech

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Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper

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“…The Golden Age of Data: Media Analytics in Study and Practice (Grady, 2019) paints the era of DS as one that requires knowing a range of skills, including research methods, familiarity with statistical procedures, and a mindset to provide insights and apply findings. In Data Science Thinking , Cao (2018) presents a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective.…”

Section: What Is Data Science? What Are the Stages Of Its Evolvement?mentioning

confidence: 99%

Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis

Urs

Minhaj²

2022

Asso for Info Science & Tech

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Data Science (DS) has emerged from the shadows of its parents-statistics and computer science-into an independent field since its origin nearly six decades ago. Its evolution and education have taken many sharp turns. We present an impressionistic study of the evolution of DS anchored to Kuhn's four stages of paradigm shifts. First, we construct the landscape of DS based on curriculum analysis of the 32 iSchools across the world offering graduate-level DS programs. Second, we paint the "field" as it emerges from the word frequency patterns, ranking, and clustering of course titles based on text mining. Third, we map the curriculum to the landscape of DS and project the same onto the Edison Data Science Framework (2017) and ACM Data Science Knowledge Areas (2021). Our study shows that the DS programs of iSchools align well with the field and correspond to the Knowledge Areas and skillsets. iSchool's DS curriculums exhibit a bias toward "data visualization" along with machine learning, data mining, natural language processing, and artificial intelligence; go light on statistics; slanted toward ontologies and health informatics; and surprisingly minimal thrust toward eScience/research data management, which we believe would add a distinctive iSchool flavor to the DS.

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Data Science Education

Cited by 7 publications

References 24 publications

Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice

Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice

Data science curriculum in the iField

Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis

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