Studies the epistemological foundations of data science, in depth Presents a defense of inductivism and an inductivist framework Offers an elaboration of a variational approach to induction

Autorentext

Wolfgang Pietsch is a philosopher of science and technology with a background in physics, affiliated with the Munich Center for Technology in Society of Technical University Munich. His main research interest is scientific method, examining scientific practice in different disciplines, in particular the engineering sciences and data science. He works on fundamental concepts like causation and probability as well as different inductive methods, in particular analogical inferences and variational approaches to induction. Wolfgang was a Poiesis Fellow of the Institute for Public Knowledge of New York University and has co-directed for many years the working group on philosophy of physics of the German Physical Society. See also his website www.wolfgangpietsch.de.

Zusammenfassung
"Readers are taken on a journey where they will discover step-by-step methodologies for data-driven research. Judiciously, each key concept of data science is concisely defined, and examples and the when, why, and how to use them are provided. ... I fully recommend it." (Thierry Edoh, Computing Reviews, February 7, 2023)

Inhalt

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. <p