The importance and relevance of the discipline of statistics with the merits of the evolving field of data science continues to be debated in academia and industry. Following a narrative literature review with over 100 scholarly and practitioner-oriented publications from statistics and data science, this article generates a pragmatic perspective on the relationships and differences between statistics and data science. Some data scientists argue that statistics is not necessary for data science as statistics delivers simple explanations and data science delivers results. Therefore, this article aims to stimulate debate and discourse among both academics and practitioners in these fields. The findings reveal the need for stakeholders to accept the inherent advantages and disadvantages within the science of statistics and data science. The science of statistics enables data science (aiding its reliability and validity), and data science expands the application of statistics to Big Data. Data scientists should accept the contribution and importance of statistics and statisticians must humbly acknowledge the novel capabilities made possible through data science and support this field of study with their theoretical and pragmatic expertise. Indeed, the emergence of data science does pose a threat to statisticians, but the opportunities for synergies are far greater.

统计学学科与不断发展的数据科学领域的优点的重要性和相关性在学术界和工业界继续争论不休。在对来自统计和数据科学的 100 多篇学术和从业者出版物进行叙述性文献回顾之后，本文对统计和数据科学之间的关系和差异提出了务实的观点。一些数据科学家认为，数据科学不需要统计，因为统计可以提供简单的解释，而数据科学可以提供结果。因此，本文旨在激发这些领域的学者和从业者之间的辩论和讨论。调查结果表明，利益相关者需要接受统计科学和数据科学的固有优势和劣势。统计科学使数据科学成为可能（有助于其可靠性和有效性），而数据科学将统计学的应用扩展到大数据。数据科学家应该接受统计学的贡献和重要性，统计学家必须谦虚地承认数据科学带来的新能力，并以其理论和务实的专业知识支持这一研究领域。事实上，数据科学的出现确实对统计学家构成了威胁，但协同作用的机会要大得多。数据科学家应该接受统计学的贡献和重要性，统计学家必须谦虚地承认数据科学带来的新能力，并以其理论和务实的专业知识支持这一研究领域。事实上，数据科学的出现确实对统计学家构成了威胁，但协同作用的机会要大得多。数据科学家应该接受统计学的贡献和重要性，统计学家必须谦虚地承认数据科学带来的新能力，并以其理论和务实的专业知识支持这一研究领域。事实上，数据科学的出现确实对统计学家构成了威胁，但协同作用的机会要大得多。