On the epistemology of data science : conceptual tools for a new inductivism / Wolfgang Pietsch.
2022
BD161 .P54 2022
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Title
On the epistemology of data science : conceptual tools for a new inductivism / Wolfgang Pietsch.
ISBN
9783030864422 (electronic bk.)
3030864421 (electronic bk.)
9783030864415
3030864413
3030864421 (electronic bk.)
9783030864415
3030864413
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-3-030-86442-2 doi
Call Number
BD161 .P54 2022
Dewey Decimal Classification
121
Summary
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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Includes bibliographical references and index.
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Description based on print version record.
Series
Philosophical studies series ; v. 148.
Available in Other Form
On the Epistemology of Data Science.
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Table of Contents
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.
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.