What is a Data Scientist? Analysis of core soft and technical competencies in job postings

Silveira, Carolina Coelho da; Marcolin, Carla Bonato; Silva, Matheus da; Domingos, Jean Carlos

doi:10.5585/iptec.v8i1.17263

Cited by 5 publications

(5 citation statements)

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“…These can undoubtedly be beneficial in introducing the field of data science and imparting its principles to students. However, working with large data sets and various languages and analysis tools is important for preparing students for the jobs of data scientists whose work is often based on big data and advanced analytics (Coelho da Silveira et al, 2020;Zhang et al, 2017). Large data sets demonstrate how programming techniques, an essential part of data science, are necessary; manual techniques that might be workable for a small data set are not practical in the work of the field.…”

Section: Discussionmentioning

confidence: 99%

Understanding the Data in K-12 Data Science

Israel-Fishelson,

Moon,

Tabak

et al. 2024

Harvard Data Science Review

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Our increasingly data-driven world is amplifying the need for everyone to develop foundational data literacy skills. In response, a growing number of K-12 data science curricula are being designed to introduce all students to data. These curricula define what data science is at the high school level and directly shape how students are introduced to and understand the discipline. Ensuring these curricula are effective, engaging, and, most critically, equitable is of paramount importance. This article presents a qualitative analysis of four curricula, focusing on the data used to introduce learners to the field of data science. The analysis uses a series of analytical lenses to evaluate the 296 distinct data sets used across the curricula and identifies trends and best practices in data set selection. The analysis includes using data collected from high school students about their interests and experiences with data to understand if and how contemporary data science curricula are tapping into students' lived experiences to situate data science learning experiences. The findings show that the curricula use relatively recent and small data sets covering a range of topics and that learner involvement in data set selection is limited. Further, the analysis reveals gaps between the data sets used and students' selfreported interests. This work highlights the importance of data set selection, especially as it relates to supporting learners from historically excluded populations in technology fields. Finally, this article provides practical implications to assess existing curricula and advances our understanding of how to situate the field of data science in the interests, ideas, and values of today's students.

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Section: Discussionmentioning

confidence: 99%

Understanding the Data in K-12 Data Science

Israel-Fishelson,

Moon,

Tabak

et al. 2024

Harvard Data Science Review

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“…iii. ANALYSIS OF CURRENT APPROACHES Silveira et al (2020) also used other search terms for the data scientist, including 'computer scientist' and 'big data consultant'. The field of data scient is very broad and diverse.…”

Section: Related Work I Competency Frameworkmentioning

confidence: 99%

“…Job advertisement analysis approaches are gaining attention (Almaleh et al 2019;Boselli et al 2018;Khaouja et al 2019;Khobreh et al 2015;Lima, Bakhshi, et al 2018;Wowczko 2015;Zhao et al 2015) especially in the field of analyzing data science competencies (Dadzie et al 2018;Djumalieva, Lima, Sleeman, et al 2018;Debortoli, Müller, and Brocke 2014;Murawski and Bick 2017;E. Sibarani et al 2020;Silveira et al 2020;Shirani 2016).…”

Section: Introductionmentioning

confidence: 99%

“…): 2020) conducted a job advertisement analysis for data scientist jobs with data from the social network service LinkedIn. Soft skills and technical skills should be analyzed through a qualitative and a quantitative typology (Silveira et al 2020). Furthermore, according to their research, 'most companies do not care about the degree and education level of the candidate, but about the necessary soft skills and technical competences' (ibid.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Job Advertisement Analysis for Assessing Data Science Competencies

Vogt,

Voigt,

Nowak

et al. 2023

Data Science Journal

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Data science competencies receive rising attention. How can competency profiles be built for data scientists? This question is encountered by synthesizing various competency frameworks from the literature and by conducting a job advertisement analysis. The 'Skills and Recruitment Ontology' is used as the underlying ontology for the job advertisement analysis. Therefore, over 5000 job postings were crawled from the job platform 'Jooble' and the results evaluated in a focus group. This work provides a competency data set for data science jobs. It points out newly found competencies and also provides design principles for competency frameworks.

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“…However, skills required for a data science team are multi-dimensional, including not only technical qualifications and expertise, but also a range of experience and abilities, such as coping with stress, problem-solving, collaborating and cooperating, adaptive to changes, resilience, openness, and risk awareness (Kautz et al, 2014;Halwani et al, 2021). Some studies have grouped these skills under an umbrella term "soft skill" in data science (Ismail and Abidin, 2016;da Silveira et al, 2020), even though this concept does not distinguish the range of interpersonal and nontechnical skills that are required in complex team projects. It is also unclear how broad concepts such as "soft skills" help to identify that path through which individuals contribute to an efficient data culture and a collaborative team environment.…”

Section: Introductionmentioning

confidence: 99%

Embedding data science innovations in organizations: a new workflow approach

Li,

Griffin,

Barker

et al. 2023

DCE

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There have been consistent calls for more research on managing teams and embedding processes in data science innovations. Widely used frameworks (e.g., the cross-industry standard process for data mining) provide a standardized approach to data science but are limited in features such as role clarity, skills, and cross-team collaboration that are essential for developing organizational capabilities in data science. In this study, we introduce a data workflow method (DWM) as a new approach to break organizational silos and create a multi-disciplinary team to develop, implement and embed data science. Different from current data science process workflows, the DWM is managed at the system level that shapes business operating model for continuous improvement, rather than as a function of a particular project, one single business unit, or isolated individuals. To further operationalize the DWM approach, we investigated an embedded data workflow at a mining operation that has been using geological data in a machine-learning model to stabilize daily mill production for the last 2 years. Based on the findings in this study, we propose that the DWM approach derives its capability from three aspects: (a) a systemic data workflow; (b) multi-disciplinary networks of collaboration and responsibility; and (c) clearly identified data roles and the associated skills and expertise. This study suggests a whole-of-organization approach and pathway to develop data science capability.

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What is a Data Scientist? Analysis of core soft and technical competencies in job postings

Cited by 5 publications

References 0 publications

Understanding the Data in K-12 Data Science

Understanding the Data in K-12 Data Science

Development of a Job Advertisement Analysis for Assessing Data Science Competencies

Embedding data science innovations in organizations: a new workflow approach

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