Data Science can be considered a technique or a science. As a technique, it is more interested in the “what” than in the “why” of data. It does not need theories that explain how things work, it just needs the results. As a science, however, working strictly from data and without theories contradicts the post-empiricist view of science. In this view, theories come before data and data is used to corroborate or falsify theories. Nevertheless, one of the most controversial statements about Data Science is that it is a science that can work without theories. In this conceptual paper, we focus on the science aspect of Data Science. How is Data Science as a science? We propose a three-phased view of Data Science that shows that different theories have different roles in each of the phases we consider. We focus on when theories are used in Data Science rather than the controversy of whether theories are used in Data Science or not. In the end, we will see that the statement “Data Science works without theories” is better put as “in some of its phases, Data Science works without the theories that originally motivated the creation of the data.”

中文翻译：

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是否或何时：关于在数据科学中使用理论的问题

数据科学可以被视为一门技术或一门科学。作为一种技术，它对数据的“什么”比“为什么”更感兴趣。它不需要解释事物如何运作的理论，它只需要结果。然而，作为一门科学，严格根据数据工作而没有理论与后经验主义的科学观点相矛盾。在这种观点下，理论先于数据，数据被用来证实或证伪理论。尽管如此，关于数据科学最有争议的说法之一是它是一门没有理论也能工作的科学。在这篇概念性论文中，我们专注于数据科学的科学方面。数据科学如何成为一门科学？我们提出了数据科学的三阶段视图，表明不同的理论在我们考虑的每个阶段都有不同的作用。我们关注的是理论何时用于数据科学，而不是理论是否用于数据科学的争论。最后，我们将看到“数据科学在没有理论的情况下工作”这句话更好地表述为“在其某些阶段，数据科学在没有最初激发数据创建的理论的情况下工作”。