The history of data science is deeply rooted in statistics. As far back as 1962, one of the most influential statisticians of the 20th century, John Tukey, was calling for recognition of a new science focused on learning from data. Subsequent work by the statistics community, particularly Jeff Wu (Donoho, 2015) and William Cleveland (2001), formally proposed the name “data science” and suggested academic statistics expand its boundaries (Donoho, 2015). Yet, the ensuing years have seen a significant influence from computer science, calls for data science to be recognized as a unique discipline distinct from statistics, and a fundamental reckoning with data science being a science.
The expansion of the probabilistic and inferential traditions of statistics along with the algorithmic, programming, and system-design concerns of computer science has led to a modern view of data science as an interdisciplinary field, which Blei and Smyth (2017) affectionately refer to as ‘the child of statistics and computer science’. Wing and colleagues (2018) see the defining characteristic being data science is not just about methods, but also about the use of those methods in the context of a domain. This interplay between domain and methods makes data science not merely the sum of its parts, but a distinct field with its own focus.