The Agnostic Structure of Data Science Methods

TL;DR

This paper presents a perspective that data science employs a unique, non-phenomenon-based approach where mathematical methods are applied through 'forcing' to reorganize data, emphasizing the internal structure over direct understanding of phenomena.

Contribution

It introduces a novel view of data science as a form of mathematization driven by forcing, especially relating deep learning to optimization methods, and questions traditional notions of problem-solving.

Findings

Deep learning neural networks are best understood as forcing optimization methods.

Data science methods reorganize data based on mathematical structure rather than problem relevance.

The internal structure of data science methods offers a new form of understanding.

Abstract

In this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by 'forcing', i.e. on the basis of their ability to reorganize the data for further analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within the context of forcing optimization methods. We finally explore the broader question of the appropriateness of data science methods in solving problems. We argue that this question should not be interpreted as a search for a correspondence between phenomena and specific solutions found by data science methods; rather, it is the internal structure of data science methods that is open to precise forms of understanding.