國立高雄大學圖資館 |

登入

回首頁

On the epistemology of data scienceconceptual tools for a new inductivism /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On the epistemology of data scienceby Wolfgang Pietsch.
其他題名:	conceptual tools for a new inductivism /
作者:	Pietsch, Wolfgang.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xviii, 295 p. :digital ;25 cm.
Contained By:	Springer Nature eBook
標題:	Knowledge, Theory of.
電子資源:	https://doi.org/10.1007/978-3-030-86442-2
ISBN:	9783030864422$q(electronic bk.)

On the epistemology of data scienceconceptual tools for a new inductivism /
Pietsch, Wolfgang.

On the epistemology of data scienceconceptual tools for a new inductivism /[electronic resource] :by Wolfgang Pietsch. - Cham :Springer International Publishing :2022. - xviii, 295 p. :digital ;25 cm. - Philosophical studies series,v. 1482542-8349 ;. - Philosophical studies series ;v.121..

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.

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.

ISBN: 9783030864422$q(electronic bk.)

Standard No.: 10.1007/978-3-030-86442-2doiSubjects--Topical Terms:

178850
Knowledge, Theory of.

LC Class. No.: BD161 / .P54 2022

Dewey Class. No.: 121

On the epistemology of data scienceconceptual tools for a new inductivism /
LDR:03158nmm a2200337 a 4500 001 619943
003 DE-He213
005 20211210131712.0
006 m d
007 cr nn 008maaau
008 221103s2022 sz s 0 eng d
020 $a 9783030864422$q(electronic bk.)
020 $a 9783030864415$q(paper)
024 7 $a 10.1007/978-3-030-86442-2 $2 doi
035 $a 978-3-030-86442-2
040 $a GP $c GP
041 0 $a eng
050 4 $a BD161 $b .P54 2022
072 7 $a HP $2 bicssc
072 7 $a PHI021000 $2 bisacsh
072 7 $a QD $2 thema
082 0 4 $a 121 $2 23
090 $a BD161 $b .P626 2022
100 1 $a Pietsch, Wolfgang. $3 920490
245 1 0 $a On the epistemology of data science $h [electronic resource] : $b conceptual tools for a new inductivism / $c by Wolfgang Pietsch.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xviii, 295 p. : $b digital ; $c 25 cm.
490 1 $a Philosophical studies series, $x 2542-8349 ; $v v. 148
505 0 $a 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.
520 $a 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.
650 0 $a Knowledge, Theory of. $3 178850
650 0 $a Research. $3 214540
650 0 $a Data structures (Computer science) $3 183917
650 0 $a Methodology. $3 177620
650 0 $a Reasoning. $3 186192
650 0 $a Analysis (Philosophy) $3 177619
650 1 4 $a Philosophy of Technology. $3 274756
650 2 4 $a Data Structures and Information Theory. $3 825714
650 2 4 $a Data-driven Science, Modeling and Theory Building. $3 758833
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Analytic Philosophy. $3 739906
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Philosophical studies series ; $v v.121. $3 679162
856 4 0 $u https://doi.org/10.1007/978-3-030-86442-2
950 $a Religion and Philosophy (SpringerNature-41175)