The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development
In Data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the Da...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2504-2289/2/2/14 |
| _version_ | 1819036654091894784 |
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| author | Fionn Murtagh Keith Devlin |
| author_facet | Fionn Murtagh Keith Devlin |
| author_sort | Fionn Murtagh |
| collection | DOAJ |
| description | In Data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the Data Science theme are described. The following are covered in this article: the rapidly growing post-graduate university course provisioning for Data Science; a preliminary study of employability requirements, and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of Big Data analytics, relating to data aggregation and scale effects. Associated also with Data Science is how direct and indirect outcomes and consequences of Data Science include decision support and policy making, and both qualitative as well as quantitative outcomes. For such reasons, the importance is noted of how Data Science builds collaboratively on other domains, potentially with innovative methodologies and practice. Further sections point towards some of the most major current research issues. |
| first_indexed | 2024-12-21T08:08:57Z |
| format | Article |
| id | doaj.art-4ff400cf34e1476bbafd920d37fe6b9b |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-12-21T08:08:57Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-4ff400cf34e1476bbafd920d37fe6b9b2022-12-21T19:10:42ZengMDPI AGBig Data and Cognitive Computing2504-22892018-06-01221410.3390/bdcc2020014bdcc2020014The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable DevelopmentFionn Murtagh0Keith Devlin1Centre of Mathematics and Data Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UKH-STAR Institute, Stanford University, Ventura Hall, 220 Panama Street, Stanford, CA 94305-4101, USAIn Data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the Data Science theme are described. The following are covered in this article: the rapidly growing post-graduate university course provisioning for Data Science; a preliminary study of employability requirements, and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of Big Data analytics, relating to data aggregation and scale effects. Associated also with Data Science is how direct and indirect outcomes and consequences of Data Science include decision support and policy making, and both qualitative as well as quantitative outcomes. For such reasons, the importance is noted of how Data Science builds collaboratively on other domains, potentially with innovative methodologies and practice. Further sections point towards some of the most major current research issues.http://www.mdpi.com/2504-2289/2/2/14big data training and learningcompany and business requirementsethicsimpactdecision supportdata engineeringopen datasmart homessmart citiesIoT |
| spellingShingle | Fionn Murtagh Keith Devlin The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development Big Data and Cognitive Computing big data training and learning company and business requirements ethics impact decision support data engineering open data smart homes smart cities IoT |
| title | The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development |
| title_full | The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development |
| title_fullStr | The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development |
| title_full_unstemmed | The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development |
| title_short | The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development |
| title_sort | development of data science implications for education employment research and the data revolution for sustainable development |
| topic | big data training and learning company and business requirements ethics impact decision support data engineering open data smart homes smart cities IoT |
| url | http://www.mdpi.com/2504-2289/2/2/14 |
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