Whether or when: The question on the use of theories in data science
本文探讨数据科学作为一门科学是否需要理论，提出三阶段观点，说明不同阶段理论扮演不同角色，并指出“数据科学无需理论”的说法应修正为“在某些阶段，数据科学无需最初驱动数据收集的理论”。
Abstract 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.”