
Big Data
Wolfgang Pietsch(Author)
Cambridge University Press
Published on 18. February 2021
Book
Paperback/Softback
86 pages
978-1-108-70669-8 (ISBN)
Description
Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises
Dimensions
Height: 225 mm
Width: 147 mm
Thickness: 6 mm
Weight
136 gr
ISBN-13
978-1-108-70669-8 (9781108706698)
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Content
1. Introduction; 2. Defining Big Data; 3. Inductivism; 4. Machine Learning as Variational Induction; 5. Correlation and Causation; 6. The Role of Theory; 7. Novel Scientific Methodology?; 8. Conclusion.