Preprint Review Version 1 This version is not peer-reviewed

Data Science in Education, Employment, Research: Data Revolution for Sustainable Development

Version 1 : Received: 28 May 2018 / Approved: 29 May 2018 / Online: 29 May 2018 (08:45:52 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Big Data Cogn. Comput. 2018, 2, 14
DOI: 10.3390/bdcc2020014

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, described are the origins and many evolutions of the Data Science theme. 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.

Subject Areas

big data training and learning; company and business requirements; ethics; impact; decision support; data engineering; open data; smart homes; smart cities; IoT

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.