Data science and the art of modelling

TL;DR

This paper emphasizes that data science should complement traditional scientific modeling rather than replace it, highlighting the importance of combining data with modeling for effective insights.

Contribution

It argues against viewing data as a substitute for scientific models and advocates for integrating data science with the art of modeling.

Findings

Data science alone cannot replace scientific modeling.

Combining data with modeling enhances scientific understanding.

Historical examples illustrate the synergy between data and models.

Abstract

Datacentric enthusiasm is growing strong across a variety of domains. Whilst data science asks unquestionably exciting scientific questions, we argue that its contributions should not be extrapolated from the scientific context in which they originate. In particular we suggest that the simple-minded idea to the effect that data can be seen as a replacement for scientific modelling is not tenable. By recalling some well-known examples from dynamical systems we conclude that data science performs at its best when coupled with the subtle art of modelling.