TY - BOOK ID - 96665821 TI - On the Epistemology of Data Science : Conceptual Tools for a New Inductivism PY - 2022 SN - 3030864421 3030864413 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Knowledge, Theory of. KW - Analysis (Philosophy) KW - Mathematical statistics. KW - Mathematics KW - Statistical inference KW - Statistics, Mathematical KW - Statistics KW - Probabilities KW - Sampling (Statistics) KW - Epistemology KW - Theory of knowledge KW - Philosophy KW - Psychology KW - Statistical methods KW - Technology KW - Data structures (Computer science). KW - Information theory. KW - System theory. KW - Computer science KW - Analysis (Philosophy). KW - Philosophy of Technology. KW - Data Structures and Information Theory. KW - Complex Systems. KW - Probability and Statistics in Computer Science. KW - Analytic Philosophy. KW - Philosophy. KW - Mathematics. KW - Analysis, Linguistic (Philosophy) KW - Analysis, Logical KW - Analysis, Philosophical KW - Analytic philosophy KW - Analytical philosophy KW - Linguistic analysis (Philosophy) KW - Logical analysis KW - Philosophical analysis KW - Philosophy, Analytical KW - Language and languages KW - Methodology KW - Logical positivism KW - Semantics (Philosophy) KW - Computer mathematics KW - Electronic data processing KW - Systems, Theory of KW - Systems science KW - Science KW - Communication theory KW - Communication KW - Cybernetics KW - Information structures (Computer science) KW - Structures, Data (Computer science) KW - Structures, Information (Computer science) KW - File organization (Computer science) KW - Abstract data types (Computer science) KW - Technology and civilization KW - Data structures (Computer science) UR - https://www.unicat.be/uniCat?func=search&query=sysid:96665821 AB - 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. . ER -