17), however, we propose computational thinking must be viewed in the context of up-to-date technologies. Like other educators (for example, Yadav et al. Computational thinking deals with cognitive skills and can, therefore, be implemented independent of technology. Some of these common skills are problem formulation, problem decomposition, organization and logical analysis of data, data representation using models and simulations, abstraction, suggestion and assessment of multiple solutions to a given problem, implementation of the chosen solution, and generalization. The discussion about computational thinking was reopened by Wing in 2006 and the term has since been interpreted through different prisms as a collection of various skills required for problem solving. In this Viewpoint, we first present our interpretation of the concept of data thinking and then, based on insights gained from the discussion about data thinking, we propose a timely need has emerged to introduce data thinking into computer science education along with computational thinking, in the context of various real-world domains using real-life data.Ĭomputational thinking is commonly defined as a set of cognitive and social skills that are applied in problem-solving processes. 15 As it turns out, exploring the novelty of data thinking uncovers new facets of computational thinking. 4 Just as data science integrates knowledge and skills from computer science, statistics, and a real-world application domain, data thinking, we propose, integrates computational thinking, statistical thinking, and domain thinking.Ĭomputational thinking was first introduced by Papert 13 and, a quarter of a century later, was illuminated and elaborated on by Wing. Recent years have seen the integration of computer science, mathematics a and statistics, together with real-world domain knowledge, into a new research and applications field: data science.
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