Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Stadelmann, Thilo | - |
dc.contributor.author | Stockinger, Kurt | - |
dc.contributor.author | Braschler, Martin | - |
dc.contributor.author | Cieliebak, Mark | - |
dc.contributor.author | Baudinot, Gerold | - |
dc.contributor.author | Dürr, Oliver | - |
dc.contributor.author | Ruckstuhl, Andreas | - |
dc.date.accessioned | 2018-03-22T10:06:06Z | - |
dc.date.available | 2018-03-22T10:06:06Z | - |
dc.date.issued | 2013 | - |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/4172 | - |
dc.description.abstract | Google Trends and other IT fever charts rate Data Science among the most rapidly emerging and promising fields that expand around computer science. Although Data Science draws on content from established fields like artificial intelligence, statistics, databases, visualization and many more, industry is demanding for trained data scientists that no one seems able to deliver. This is due to the pace at which the field has expanded and the corresponding lack of curricula; the unique skill set, which is inherently multi-disciplinary; and the translation work (from the US web economy to other ecosystems) necessary to realize the recognized world-wide potential of applying analytics to all sorts of data. In this contribution we draw from our experiences in establishing an inter-disciplinary Data Science lab in order to highlight the challenges and potential remedies for Data Science in Europe. We discuss our role as academia in the light of the potential societal/economic impact as well as the challenges in organizational leadership tied to such inter-disciplinary work. | de_CH |
dc.language.iso | en | de_CH |
dc.rights | Licence according to publishing contract | de_CH |
dc.subject | Data science | de_CH |
dc.subject | INIT | de_CH |
dc.subject | Multi-disciplinary | de_CH |
dc.subject | Fintech | de_CH |
dc.subject.ddc | 004: Informatik | de_CH |
dc.subject.ddc | 020: Bibliotheks- und Informationswissenschaft | de_CH |
dc.title | Applied data science in Europe : challenges for academia in keeping up with a highly demanded topic | de_CH |
dc.type | Konferenz: Paper | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | School of Engineering | de_CH |
zhaw.organisationalunit | Institut für Angewandte Informationstechnologie (InIT) | de_CH |
zhaw.conference.details | 9th European Computer Science Summit, Amsterdam, Niederlande, 8-9 October 2013 | de_CH |
zhaw.funding.eu | No | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.publication.status | publishedVersion | de_CH |
zhaw.publication.review | Peer review (Publikation) | de_CH |
zhaw.title.proceedings | Proceedings of the 9th European Computer Science Summit | de_CH |
zhaw.webfeed | FinTech | de_CH |
zhaw.webfeed | Software Systems | de_CH |
zhaw.webfeed | Computer Vision, Perception and Cognition | de_CH |
Appears in collections: | Publikationen School of Engineering |
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