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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3176
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dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorStockinger, Kurt-
dc.contributor.authorHeinatz-Bürki, Gundula-
dc.contributor.authorBraschler, Martin-
dc.date.accessioned2019-07-04T12:52:32Z-
dc.date.available2019-07-04T12:52:32Z-
dc.date.issued2019-06-14-
dc.identifier.isbn978-3-030-11821-1de_CH
dc.identifier.isbn978-3-030-11820-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17426-
dc.description.abstractWhat is a data scientist? How can you become one? How can you form a team of data scientists that fits your organization? In this chapter, we trace the skillset of a successful data scientist and define the necessary competencies. We give a disambiguation to other historically or contemporary definitions of the term, and show how a career as a data scientist might get started. Finally we will answer the above mentioned third question, i.e. how to build analytics teams within a data-driven organization.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofApplied data science : lessons learned for the data-driven businessde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectTeamde_CH
dc.subjectOrganizationde_CH
dc.subjectEducationde_CH
dc.subjectSkillsetde_CH
dc.subjectData sciencede_CH
dc.subjectHistoryde_CH
dc.subjectCareer developmentde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleData scientistsde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-11821-1_3de_CH
dc.identifier.doi10.21256/zhaw-3176-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end45de_CH
zhaw.pages.start31de_CH
zhaw.parentwork.editorBraschler, Martin-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.parentwork.editorStockinger, Kurt-
zhaw.publication.statussubmittedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.author.additionalNode_CH
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Stadelmann, T., Stockinger, K., Heinatz-Bürki, G., & Braschler, M. (2019). Data scientists. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 31–45). Springer. https://doi.org/10.1007/978-3-030-11821-1_3
Stadelmann, T. et al. (2019) ‘Data scientists’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 31–45. Available at: https://doi.org/10.1007/978-3-030-11821-1_3.
T. Stadelmann, K. Stockinger, G. Heinatz-Bürki, and M. Braschler, “Data scientists,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 31–45. doi: 10.1007/978-3-030-11821-1_3.
STADELMANN, Thilo, Kurt STOCKINGER, Gundula HEINATZ-BÜRKI und Martin BRASCHLER, 2019. Data scientists. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 31–45. ISBN 978-3-030-11821-1
Stadelmann, Thilo, Kurt Stockinger, Gundula Heinatz-Bürki, and Martin Braschler. 2019. “Data Scientists.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 31–45. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_3.
Stadelmann, Thilo, et al. “Data Scientists.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 31–45, https://doi.org/10.1007/978-3-030-11821-1_3.


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