000			a
999			_c31014 _d31014
008			220610b xxu||||| |||| 00| 0 eng d
020			_a9783030864415
082			_a121 _bPIE
100			_aPietsch, Wolfgang
245			_aOn the epistemology of data science : conceptual tools for a new inductivism
260			_bSpringer, _c2022 _aCham :
300			_axviii, 295 p. ; _c24 cm
365			_b99.99 _cEUR _d86.00
490			_aPhilosophical studies series _vv.148
504			_aIncludes bibliographical references and index.
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.
650			_aAnalogy
650			_a Bacon's tables
650			_aDeterminism
650			_aCusation
650			_aCausation
650			_aDecision trees
650			_aExperimentation
650			_aGru problem
650			_aIndifference principle
650			_aK-means clustering
650			_aCeteris Paribus laws
650			_a Mill's method
650			_a Non-redundancy principle
650			_aOverdetermination,causation
942			_2ddc _cBK