dc.contributor.author | Mikalef, Patrick | |
dc.contributor.author | Giannakos, Michail | |
dc.contributor.author | Pappas, Ilias | |
dc.contributor.author | Krogstie, John | |
dc.date.accessioned | 2019-03-28T13:19:39Z | |
dc.date.available | 2019-03-28T13:19:39Z | |
dc.date.created | 2018-10-12T11:11:31Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | IEEE Global Engineering Education Conference, EDUCON. 2018, 503-512. | nb_NO |
dc.identifier.issn | 2165-9559 | |
dc.identifier.uri | http://hdl.handle.net/11250/2592255 | |
dc.description.abstract | It is widely recognized by public and private organizations, that the biggest challenge faced in light of the data revolution is finding people with the required set of skills to transform data into actionable insight. The growing interest on the role of the data scientist and the relating data analytics skills has seen an increasing amount of research on the importance of data analytics skills in the contemporary working environment. Yet, there is still limited understanding on the importance of data analytic skills, and even more, there is limited research on the discrepancies between the skills that are needed in the market and what graduates possess. To this end, this research uses a mixed-methods approach combining quantitative survey data from 113 IT executives, and qualitative interview data from 27 big data project managers to explore the significance, discrepancies, and aspects of data analytic skills. Our results show that data analytic skills significantly contribute firm performance, particularly for firms that are data-oriented. In addition, we find that the need for skills greatly exceeds those that graduates possess. Lastly, our analysis suggests that the data skills of the data scientist span multiple subject areas which are further discussed. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.title | The human side of big data: Understanding the skills of the data scientist in education and industry | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 503-512 | nb_NO |
dc.source.journal | IEEE Global Engineering Education Conference, EDUCON | nb_NO |
dc.identifier.doi | 10.1109/EDUCON.2018.8363273 | |
dc.identifier.cristin | 1619935 | |
dc.description.localcode | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |