On the Epistemology of Data Science: Conceptual Tools for a New Inductivism

This book explores the epistemological foundations of data science, arguing that it is a genuine science that can stand on its own. It proposes an inductivist framework that addresses key concepts such as causation, probability, and analogy and is demonstrated to be adequate and useful for analyzing the foundations of data science. The book is of interest to computer scientists, philosophers, and data scientists of various disciplines.

Format: Paperback / softback
Length: 295 pages
Publication date: 11 December 2022
Publisher: Springer Nature Switzerland AG

This book delves into the contentious debates surrounding the epistemological foundations of data science. It explores whether data science can be considered a legitimate scientific discipline on its own or if it merely serves as a supplementary practice that can contribute to scientific progress, albeit with limitations. The author presents a comprehensive conceptual framework aimed at addressing these critical questions.

Within the pages of this book, readers will find a robust defense of inductivism, a philosophical approach that emphasizes the importance of drawing conclusions based on empirical evidence. The author also examines the arguments against inductivism and presents a nuanced epistemology of data science that, by definition, must embrace an inductive approach.

The book begins by exploring the fundamental concepts of an inductivist methodology, such as causation, probability, and analogy. It then outlines an inductivist framework that can be applied to analyze the epistemological foundations of data science. The author demonstrates that this framework is sufficient and valuable for understanding the complexities of data-driven research.

Furthermore, the book highlights the presence of many aspects of the variational rationale, a concept proposed by Federica Russos, in algorithms commonly used in data science. This connection between philosophical ideas and practical applications underscores the significance of this work for computer scientists, philosophers, and data scientists across various disciplines.

Data science is situated within a broader context of distinguishing between different types of scientific practices, including exploratory experimentation and theory-driven research. The book also explores the differences between phenomenological and theoretical science, shedding light on the unique challenges and opportunities presented by data-driven inquiry.

This philosophical perspective and conceptual framework offer valuable insights for those seeking to delve deeper into the ethical and philosophical implications of algorithms used in data science. It serves as a solid foundation for further in-depth analysis and discussion, fostering a deeper understanding of the field and its potential contributions to scientific progress.

Weight: 486g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030864446
Edition number: 1st ed. 2022