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On the epistemology of data science ...

Pietsch, Wolfgang.

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On the epistemology of data science = conceptual tools for a new inductivism /

Record Type:	Electronic resources : Monograph/item
Title/Author:	On the epistemology of data science/ by Wolfgang Pietsch.
Reminder of title:	conceptual tools for a new inductivism /
Author:	Pietsch, Wolfgang.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xviii, 295 p. :digital ;25 cm.
[NT 15003449]:	Preface -- Chapter 1. Introduction -- Chapter 2. Inductivism -- Chapter 3. Phenomenological Science -- Chapter 4. Variational Induction -- Chapter 5. Causation As Difference Making -- Chapter 6. Evidence -- Chapter 7. Concept Formation -- Chapter 8. Analogy -- Chapter 9. Causal Probability -- Chapter 10. Conclusion -- Index.
Contained By:	Springer Nature eBook
Subject:	Knowledge, Theory of. -
Online resource:	https://doi.org/10.1007/978-3-030-86442-2
ISBN:	9783030864422

On the epistemology of data science = conceptual tools for a new inductivism /
Pietsch, Wolfgang.

On the epistemology of data scienceconceptual tools for a new inductivism /[electronic resource] :by Wolfgang Pietsch. - Cham :Springer International Publishing :2022. - xviii, 295 p. :digital ;25 cm. - Philosophical studies series,v. 1482542-8349 ;. - Philosophical studies series ;v. 148..

Preface -- Chapter 1. Introduction -- Chapter 2. Inductivism -- Chapter 3. Phenomenological Science -- Chapter 4. Variational Induction -- Chapter 5. Causation As Difference Making -- Chapter 6. Evidence -- Chapter 7. Concept Formation -- Chapter 8. Analogy -- Chapter 9. Causal Probability -- Chapter 10. Conclusion -- Index.

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.

ISBN: 9783030864422

Standard No.: 10.1007/978-3-030-86442-2doiSubjects--Topical Terms:

523921
Knowledge, Theory of.

LC Class. No.: BD161 / .P54 2022

Dewey Class. No.: 121

On the epistemology of data science = conceptual tools for a new inductivism /
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520 $a 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.
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