On the Epistemology of Data Science

Beschreibung

This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed.

Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo’s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework.

The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science.

Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science. 

Studies the epistemological foundations of data science, in depth

Presents a defense of inductivism and an inductivist framework

Offers an elaboration of a variational approach to induction

Autor*in

Wolfgang Pietsch

Themen in »On the Epistemology of Data Science«

Causal approach to analogy Causation difference making Data science exploratory experimentation Data science phenomenological science Data science and explanation Epistemology of data science Refining eliminative induction Symmetries in probabilistic reasoning Variational approach to induction data science theory data science theory-driven experimentation data science inductivist framework data science analogy data science probability data science causation

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“Readers are taken on a journey where they will discover step-by-step methodologies for data-driven research. Judiciously, each key concept of data science is concisely defined, and examples and the when, why, and how to use them are provided. … I fully recommend it.” (Thierry Edoh, Computing Reviews, February 7, 2023)
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