LEADER 04560nam 22007215 450 001 9910522940303321 005 20230810173856.0 010 $a3-030-86442-1 024 7 $a10.1007/978-3-030-86442-2 035 $a(MiAaPQ)EBC6825106 035 $a(Au-PeEL)EBL6825106 035 $a(CKB)20106120200041 035 $a(DE-He213)978-3-030-86442-2 035 $a(EXLCZ)9920106120200041 100 $a20211210d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOn the Epistemology of Data Science $eConceptual Tools for a New Inductivism /$fby Wolfgang Pietsch 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (308 pages) 225 1 $aPhilosophical Studies Series,$x2542-8349 ;$v148 300 $aIncludes index. 311 08$aPrint version: Pietsch, Wolfgang On the Epistemology of Data Science Cham : Springer International Publishing AG,c2022 9783030864415 327 $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. 330 $aThis book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo?s recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science. . 410 0$aPhilosophical Studies Series,$x2542-8349 ;$v148 606 $aTechnology$xPhilosophy 606 $aData structures (Computer science) 606 $aInformation theory 606 $aSystem theory 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aAnalysis (Philosophy) 606 $aPhilosophy of Technology 606 $aData Structures and Information Theory 606 $aComplex Systems 606 $aProbability and Statistics in Computer Science 606 $aAnalytic Philosophy 615 0$aTechnology$xPhilosophy. 615 0$aData structures (Computer science) 615 0$aInformation theory. 615 0$aSystem theory. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aAnalysis (Philosophy) 615 14$aPhilosophy of Technology. 615 24$aData Structures and Information Theory. 615 24$aComplex Systems. 615 24$aProbability and Statistics in Computer Science. 615 24$aAnalytic Philosophy. 676 $a121 700 $aPietsch$b Wolfgang$f1938-$0978673 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522940303321 996 $aOn the epistemology of data science$92908602 997 $aUNINA