Data Science — definition and structural representation

This article is a continuation of the discussion on the existing meanings and formalization of the definition of “Data Science” as an autonomous discipline, field of knowledge, clarification of its defining components, integration, and interaction processes between them. It is noted that most scientific results trace the data-centric nature of the presentation and analysis of this discipline, i.e. the emphasis on the word Data. Analysis of the frequency of use of key terms in the definitions of Data Science shows what our colleagues focus on, which terms of the definitions of Data Science they are based on. In this paper, we make and argue certain additions to Drew Conway’s Data Science Venn Diagram, which does not reflect all the resources of the components that define the applied side of Data Science, and, moreover, does not reveal the interaction of these resources not from the point of view of the data researcher, nor in its global understanding. We also propose a unified structural representation of Data Science in the format of an updated Drew Conway’s Venn diagram based on a property/attribute that establishes correspondences that provide integration/interoperability between the elements of the sets of Drew Conway’s Venn diagram. The new definition of Data Science as an interdisciplinary science and methodology of presenting activities for analysis and extraction of data, information, and knowledge is substantiated.