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Big Data Cogn. Comput. 2018, 2(2), 14; https://doi.org/10.3390/bdcc2020014

The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development

1
Centre of Mathematics and Data Science, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
2
H-STAR Institute, Stanford University, Ventura Hall, 220 Panama Street, Stanford, CA 94305-4101, USA
*
Author to whom correspondence should be addressed.
Received: 28 May 2018 / Revised: 16 June 2018 / Accepted: 16 June 2018 / Published: 19 June 2018
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Abstract

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. View Full-Text
Keywords: big data training and learning; company and business requirements; ethics; impact; decision support; data engineering; open data; smart homes; smart cities; IoT big data training and learning; company and business requirements; ethics; impact; decision support; data engineering; open data; smart homes; smart cities; IoT
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Murtagh, F.; Devlin, K. The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development. Big Data Cogn. Comput. 2018, 2, 14.

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