The science of statistics versus data science: What is the future?

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.