Publication
Big data: business, technology, education, and science
| dc.contributor.author | Johnson, Jeffrey | |
| dc.contributor.author | Tesei, Luca | |
| dc.contributor.author | Piangerelli, Marco | |
| dc.contributor.author | Merelli, Emanuela | |
| dc.contributor.author | Paci, Riccardo | |
| dc.contributor.author | Stojanovic, Nedad | |
| dc.contributor.author | Leitão, Paulo | |
| dc.contributor.author | Barbosa, José | |
| dc.contributor.author | Amador, Marco | |
| dc.date.accessioned | 2020-03-30T09:10:45Z | |
| dc.date.available | 2020-03-30T09:10:45Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Transforming the latent value of big data into real value requires the great human intelligence and application of human-data scientists. Data scientists are expected to have a wide range of technical skills alongside being passionate self-directed people who are able to work easily with others and deliver high quality outputs under pressure. There are hundreds of university, commercial, and online courses in data science and related topics. Apart from people with breadth and depth of knowledge and experience in data science, we identify a new educational path to train “bridge persons” who combine knowledge of an organization’s business with sufficient knowledge and understanding of data science to “bridge” between non-technical people in the business with highly skilled data scientists who add value to the business. The increasing proliferation of big data and the great advances made in data science do not herald in an era where all problems can be solved by deep learning and artificial intelligence. Although data science opens up many commercial and social opportunities, data science must complement other science in the search for new theory and methods to understand and manage our complex world. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Johnson, Jeffrey; Tesei, Luca; Piangerelli, Marco; Merelli, Emanuela; Paci, Riccardo; Stojanovic, Nedad; Leitão, Paulo; Barbosa, José ;Amador, Marco (2018). Big data: business, technology, education, and science. Ubiquity. ISSN 1530-2180. p. 1-13 | pt_PT |
| dc.identifier.doi | 10.1145/3158350 | pt_PT |
| dc.identifier.issn | 1530-2180 | |
| dc.identifier.uri | http://hdl.handle.net/10198/21170 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Big data | pt_PT |
| dc.subject | Data science | pt_PT |
| dc.title | Big data: business, technology, education, and science | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 13 | pt_PT |
| oaire.citation.issue | July | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | Ubiquity | pt_PT |
| oaire.citation.volume | 2018 | pt_PT |
| person.familyName | Leitão | |
| person.familyName | Barbosa | |
| person.givenName | Paulo | |
| person.givenName | José | |
| person.identifier | A-8390-2011 | |
| person.identifier | 609187 | |
| person.identifier.ciencia-id | 8316-8F13-DA71 | |
| person.identifier.ciencia-id | 021B-4191-D8A5 | |
| person.identifier.orcid | 0000-0002-2151-7944 | |
| person.identifier.orcid | 0000-0003-3151-6686 | |
| person.identifier.rid | A-5468-2011 | |
| person.identifier.scopus-author-id | 35584388900 | |
| person.identifier.scopus-author-id | 48360905400 | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | article | pt_PT |
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