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

Abstract

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.

Automation of evaluation of the interlaboratory comparisons results by means of software

“…The use of computing capabilities of computers provides an opportunity to simplify data processing procedures, introduce automation of calculations and graphical display of results, make predictions, etc. [5] .…”

Folding Bicycle Prospective Buyer Prediction Model

“…In the future, data sets could be prepared with more features and using advanced machine learning techniques for model building could be used to build multi-class classification models to predict more than two types of bicycles. Another improvement is for data visualization can be done using a more sophisticated tool such as Tableau as suggested in [22].…”

The disciplinary research landscape of data science reflected in data science journals

“…However, there are not many journals (6.78%) belong to the subject of statistics. Besides computers, data science journals are mainly from the disciplines of business and economics (11.86%) and biology (8.47%), which are generally seen as the application areas of data science (Murtagh and Devlin, 2018;Provost and Fawcett, 2013). Other journals in our dataset separately belong to a wide range of disciplines such as transportation, education and environmental studies.…”