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001443320 001__ 1443320
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001443320 019__ $$a1288464336$$a1288560860$$a1288635426$$a1288669407$$a1294349147
001443320 020__ $$a9783030864422$$q(electronic bk.)
001443320 020__ $$a3030864421$$q(electronic bk.)
001443320 020__ $$z9783030864415
001443320 020__ $$z3030864413
001443320 0247_ $$a10.1007/978-3-030-86442-2$$2doi
001443320 035__ $$aSP(OCoLC)1289372749
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001443320 049__ $$aISEA
001443320 050_4 $$aBD161$$b.P54 2022
001443320 08204 $$a121$$223
001443320 1001_ $$aPietsch, Wolfgang$$c(Philosopher of science and technology),$$eauthor.
001443320 24510 $$aOn the epistemology of data science :$$bconceptual tools for a new inductivism /$$cWolfgang Pietsch.
001443320 264_1 $$aCham :$$bSpringer,$$c[2022]
001443320 264_4 $$c©2022
001443320 300__ $$a1 online resource
001443320 336__ $$atext$$btxt$$2rdacontent
001443320 337__ $$acomputer$$bc$$2rdamedia
001443320 338__ $$aonline resource$$bcr$$2rdacarrier
001443320 347__ $$atext file
001443320 347__ $$bPDF
001443320 4901_ $$aPhilosophical studies series ;$$vvolume 148
001443320 504__ $$aIncludes bibliographical references and index.
001443320 5050_ $$aPreface -- Chapter 1. Introduction -- Chapter 2. Inductivism -- Chapter 3. Phenomenological Science -- Chapter 4. Variational Induction -- Chapter 5. Causation As Difference Making -- Chapter 6. Evidence -- Chapter 7. Concept Formation -- Chapter 8. Analogy -- Chapter 9. Causal Probability -- Chapter 10. Conclusion -- Index.
001443320 506__ $$aAccess limited to authorized users.
001443320 520__ $$aThis book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo's recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science.
001443320 588__ $$aDescription based on print version record.
001443320 650_0 $$aKnowledge, Theory of.
001443320 650_0 $$aResearch.
001443320 650_0 $$aMethodology.
001443320 650_0 $$aReasoning.
001443320 650_6 $$aThéorie de la connaissance.
001443320 650_6 $$aRecherche.
001443320 650_6 $$aMéthodologie.
001443320 655_0 $$aElectronic books.
001443320 77608 $$iPrint version:$$aPietsch, Wolfgang.$$tOn the Epistemology of Data Science.$$dCham : Springer International Publishing AG, ©2022$$z9783030864415
001443320 830_0 $$aPhilosophical studies series ;$$vv. 148.
001443320 852__ $$bebk
001443320 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-86442-2$$zOnline Access$$91397441.1
001443320 909CO $$ooai:library.usi.edu:1443320$$pGLOBAL_SET
001443320 980__ $$aBIB
001443320 980__ $$aEBOOK
001443320 982__ $$aEbook
001443320 983__ $$aOnline
001443320 994__ $$a92$$bISE