Perspective on Data Science

TL;DR

This paper discusses the broad and evolving nature of data science, emphasizing the need to identify core principles to better teach and develop the field.

Contribution

It proposes distilling core ideas from data science through the iterative analysis process to develop a unifying theory and improve education.

Findings

Identifies the need for a core set of principles in data science.

Suggests generalizations from past experience can form a theoretical basis.

Highlights the importance of focusing on the iterative analysis process.

Abstract

The field of data science currently enjoys a broad definition that includes a wide array of activities which borrow from many other established fields of study. Having such a vague characterization of a field in the early stages might be natural, but over time maintaining such a broad definition becomes unwieldy and impedes progress. In particular, the teaching of data science is hampered by the seeming need to cover many different points of interest. Data scientists must ultimately identify the core of the field by determining what makes the field unique and what it means to develop new knowledge in data science. In this review we attempt to distill some core ideas from data science by focusing on the iterative process of data analysis and develop some generalizations from past experience. Generalizations of this nature could form the basis of a theory of data science and would serve to unify and scale the teaching of data science to large audiences.