@article{1443320,
      recid = {1443320},
      author = {Pietsch, Wolfgang},
      title = {On the epistemology of data science : conceptual tools for  a new inductivism /},
      pages = {1 online resource},
      abstract = {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.},
      url = {http://library.usi.edu/record/1443320},
      doi = {https://doi.org/10.1007/978-3-030-86442-2},
}