Fundamentals of Data Science for Future Data Scientists

Chen, Jiangping; Ayala, Brenda Reyes; Alsmadi, Duha; Wang, Guonan

doi:10.1201/9781315209555-6

Cited by 8 publications

(4 citation statements)

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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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“…The whole pack of mathematical techniques have been integrated into computer schemes, accelerating the outcomes towards a more data centered conclusions. Figure 1 shows an overall view of the DS scope [6]. Imagine an enterprise received a large amount of data, from different sources, like sensors, human observations and so on.…”

Section: Data Science: What's For?mentioning

confidence: 99%

Application of Data Science in the Sustainable Energy Industry

Marques¹

2022

Uncertainty Modeling: Fundamental Concepts and Models

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The application of Data Science (DS) in engineering fields is presented from a broad approach, taking the basics and historical motivation to apply statistical tools into practical needs, from Health Sciences to structural monitoring, the latter linked to the sustainable energy context, like solar, wind, geothermal and nuclear. Moreover, brief bullets about the DS techniques and models are also covered, most in the Machine Learning branch, but with some indications of Artificial Intelligence applications. Math techniques like Kringing, Support Vector Machines, Cross-Validation, Data Regularization, Surrogate Models, for instance, also make part of the text, where the practical issues are evaluated in a brief technical manner. The chapter covers the structural reliability and its DS application too.

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“…De maneira geral, a ciência de dados é compreendida como a extração metodológica de conhecimento a partir de quantidades massivas de dados (Dhar, 2013;NIST Big Data Public Working Group, 2015). Uma visão mais abrangente é apresentada por Chen et al (2018) quando definem a ciência de dados como um campo interdisciplinar que visa beneficiar os seres humanos, por meio da combinação da metodologia científica e da tecnologia computacional voltada à gestão, acesso, análise e avaliação dos dados. Reforçando essa interdisciplinaridade, Grossi et al (2021) afirmam que a ciência de dados combina diferentes teorias, práticas e modelos, configurando-se em um paradigma pervasivo que envolve múltiplas disciplinas, com potencial inovativo para a ciência, indústria, política e, consequentemente, para a vida das pessoas.…”

Section: Ciência De Dadosunclassified

Requisitos para a ciência de dados: analisando anúncios de vagas de emprego com mineração de texto

Guimarães¹,

Júnior²,

Freitas

2022

risti

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Esta pesquisa identifica os requisitos para cientistas de dados no Brasil em anúncios de emprego. Para analisar estes documentos, adota métodos de mineração de texto: n-grama, modelagem de tópico e agrupamento. Os resultados apontam uma concentração de vagas em São Paulo e revelam que a modalidade remota é a segunda mais ofertada. Além disso, destaca que os salários no Brasil estão abaixo da média de outros países, mesmo que as organizações procurem por profissionais experientes e com alto nível educacional. Quanto aos requisitos, há o predomínio de habilidades técnicas como machine learning, modelos estatísticos, python, banco de dados, dentre outras. Para as técnicas de mineração, demonstra que n-grama e o agrupamento são mais adequadas que a modelagem de tópicos.

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Fundamentals of Data Science for Future Data Scientists

Cited by 8 publications

References 0 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

Application of Data Science in the Sustainable Energy Industry

Requisitos para a ciência de dados: analisando anúncios de vagas de emprego com mineração de texto

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