Journal:The development of data science: Implications for education, employment, research, and the data revolution for sustainable development
| Full article title | The development of data science: Implications for education, employment, research, and the data revolution for sustainable development |
|---|---|
| Journal | Big Data and Cognitive Computing |
| Author(s) | Murtagh, Fionn; Devlin, Keith |
| Author affiliation(s) | University of Huddersfield, Stanford University |
| Primary contact | Email: fmurtagh at acm dot org |
| Year published | 2018 |
| Volume and issue | 2(2) |
| Page(s) | 14 |
| DOI | 10.3390/bdcc2020014 |
| ISSN | 2504-2289 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | http://www.mdpi.com/2504-2289/2/2/14/htm |
| Download | http://www.mdpi.com/2504-2289/2/2/14/pdf (PDF) |
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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 major current research issues.
Keywords: big data training and learning, company and business requirements, ethics, impact, decision support, data engineering, open data, smart homes, smart cities, IoT
Data science as the convergence and bridging of disciplines
The context of our problem-solving and analytics will always be quite fundamental and very specific and particularly oriented. Section 4 draws some interesting and relevant implications of this. This article is very oriented towards commonality and mutual influence of methodologies, and of analytical processes and procedures. A nice example of the parallel nature of such things is how Big Data analytics is often considered a synomym of Data Science. In Section 2.2, it is mentioned how public transport may well use smartphone and mobile phone wireless connection data to observe locations of individuals. This close association or, perhaps even, identity of Big Data analytics and Data Science will have growing importance with the Internet of Things, and smart cities and smart homes, and so on, as noted in Section 8. Here is an outstanding company perspective on this: Ref [1] “Five years ago, the McKinsey Global Institute (MGI) released Big data: The next frontier for innovation, competition, and productivity. In the years since, data science has continued to make rapid advances ⋯”.
References
Notes
This presentation is faithful to the original, with only a few minor changes to grammar, spelling, and presentation, including the addition of PMCID and DOI when they were missing from the original reference.
