Skip to main content
  • Book
  • © 2022

On the Epistemology of Data Science

Conceptual Tools for a New Inductivism

  • 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

Part of the book series: Philosophical Studies Series (PSSP, volume 148)

Buying options

eBook USD 89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-86442-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 119.99
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (10 chapters)

  1. Front Matter

    Pages i-xviii
  2. Introduction

    • Wolfgang Pietsch
    Pages 1-10
  3. Inductivism

    • Wolfgang Pietsch
    Pages 11-36
  4. Phenomenological Science

    • Wolfgang Pietsch
    Pages 37-71
  5. Variational Induction

    • Wolfgang Pietsch
    Pages 73-107
  6. Causation as Difference Making

    • Wolfgang Pietsch
    Pages 109-173
  7. Evidence

    • Wolfgang Pietsch
    Pages 175-188
  8. Concept Formation

    • Wolfgang Pietsch
    Pages 189-200
  9. Analogy

    • Wolfgang Pietsch
    Pages 201-234
  10. Causal Probability

    • Wolfgang Pietsch
    Pages 235-287
  11. Conclusion

    • Wolfgang Pietsch
    Pages 289-290
  12. Back Matter

    Pages 291-295

About this book

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. 

Keywords

  • 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
  • data science inductivist methodology
  • data science epistemology
  • foundations of data science
  • Federica Russo inductive methodology

Authors and Affiliations

  • Munich Center for Technology in Society, Technical University of Munich, Munich, Germany

    Wolfgang Pietsch

About the author

Wolfgang Pietsch is a philosopher of science and technology with a background in physics, affiliated with the Munich Center for Technology in Society of Technical University Munich. His main research interest is scientific method, examining scientific practice in different disciplines, in particular the engineering sciences and data science. He works on fundamental concepts like causation and probability as well as different inductive methods, in particular analogical inferences and variational approaches to induction. Wolfgang was a Poiesis Fellow of the Institute for Public Knowledge of New York University and has co-directed for many years the working group on philosophy of physics of the German Physical Society.  See also his website www.wolfgangpietsch.de. 

Bibliographic Information

Buying options

eBook USD 89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-86442-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 119.99
Price excludes VAT (USA)