00738nam0-22002771i-450 99000278190040332120231116090734.0000278190FED01000278190(Aleph)000278190FED0120000920d1961----km-y0itay50------baitaITLogica dell'operare in bancaprimi schemiAldo Amaduzzi2. edTorinoGiappichelli196199 p.Amaduzzi,Aldo<1904-1991>369322ITUNINARICAUNIMARCBK99000278190040332122-7-27-RA36157ECAECALogica dell'operare in banca426997UNINAING0104560nam 22007215 450 991052294030332120230810173856.03-030-86442-110.1007/978-3-030-86442-2(MiAaPQ)EBC6825106(Au-PeEL)EBL6825106(CKB)20106120200041(DE-He213)978-3-030-86442-2(EXLCZ)992010612020004120211210d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierOn the Epistemology of Data Science Conceptual Tools for a New Inductivism /by Wolfgang Pietsch1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (308 pages)Philosophical Studies Series,2542-8349 ;148Includes index.Print version: Pietsch, Wolfgang On the Epistemology of Data Science Cham : Springer International Publishing AG,c2022 9783030864415 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. .Philosophical Studies Series,2542-8349 ;148TechnologyPhilosophyData structures (Computer science)Information theorySystem theoryComputer scienceMathematicsMathematical statisticsAnalysis (Philosophy)Philosophy of TechnologyData Structures and Information TheoryComplex SystemsProbability and Statistics in Computer ScienceAnalytic PhilosophyTechnologyPhilosophy.Data structures (Computer science)Information theory.System theory.Computer scienceMathematics.Mathematical statistics.Analysis (Philosophy)Philosophy of Technology.Data Structures and Information Theory.Complex Systems.Probability and Statistics in Computer Science.Analytic Philosophy.121Pietsch Wolfgang1938-978673MiAaPQMiAaPQMiAaPQBOOK9910522940303321On the epistemology of data science2908602UNINA