Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS.

We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines.

The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities.

工程设计（ED）是解决要求和约束内的技术问题以创建新工件的过程。数据科学（DS）是跨学科领域，它使用计算系统从结构化和非结构化数据中提取知识。这两个领域之间的协同作用源远流长，在过去的几十年中，ED越来越多地受益于与DS的集成。

我们在ED和DS的交汇处进行了文献综述，确定了显示出对ED贡献最大的工具，算法和数据源，并确定了未来数据科学家和设计人员应解决的一组挑战，以最大程度地发挥潜力DS支持有效和高效的设计。自然语言处理技术已支持严格的范围界定审查方法，以便提供对两个模糊限制学科的研究的审查。

本文确定了与这两个研究领域及其接口相关的挑战。文献中的主要空白围绕着要在设计的特殊情况下应用的计算技术的适应性，对数据源的识别以促进设计研究以及对这些数据进行适当的特征化。考虑到挑战对ED阶段的影响和DS方法的适用性，对挑战进行了分类，为将来在各个领域的研究提供了地图。范围审查显示，要充分利用DS工具，必须增加设计从业人员与研究人员之间的协作，以打开新的数据驱动机会。