Abstract
Data Science has ignited unprecedented academic, industrial, and pedagogical fervor, yet its status as a \textit{science} in the classical sense---comparable to physics or biology---remains profoundly unsettled. This article interrogates the epistemological foundations of Data Science by examining its hybrid theoretical lineage, from the Universal Approximation Theorem to the No-Free-Lunch Theorems, with special emphasis on the fundamental Bayesian optimality results for both regression and classification. We argue that Data Science is in a vigorous \textit{gestational period}, characterized not by an absence of principles but by a creative tension between empirical pragmatism and deep mathematical theory. The Cross-Validation score emerges as the Swiss Army knife of computational epistemology, enabling estimation of generalization error from finite data. We analyze candidate ``laws'' (bias-variance tradeoff, Vapnik-Chervonenkis theory), present complete formulations of Bayesian optimal predictors under both squared and zero-one losses, and propose that Data Science's maturity will be marked by the codification of its beautiful, necessary tensions. The fundamental theorems of Data Science are indeed hybrid, bridging centuries of mathematical analysis with modern computational epistemology.
Publication Date
Spring 3-31-2026
Document Type
Technical Report
Department, Program, or Center
Mathematics and Statistics, School of
College
College of Science
Recommended Citation
Fokoue, Ernest, "On the Scientific Stature of Data Science: The Epistemological Unicorn" (2026). Accessed from
https://repository.rit.edu/article/2185
Campus
RIT – Main Campus
