The information society: the epistemology of big data

A.Y. 2020/2021
Overall hours
Learning objectives
The course aims at developing the logico-mathematical background to assess critically the epistemology of "big data". In particular it focusses on how the formalisation of inductive inference sheds crucial methodological light on the "datacentric" revolution, which is currently dotting the development of the natural and social sciences.
Expected learning outcomes
At the end of the course, students will

- know the central concepts and reasoning tools of discrete mathematics
- know the central concepts in elementary probability theory
- understand the epistemological questions related to inductive reasoning
- understand the relevance of a proper the epistemology of inductive inference in the wider methodological discussion on "big data"

Ability to apply knowledge and understanding
At the end of the course, students will be able to

- read and evaluate the scientific literature on inductive reasoning
- apply the tools learnt to solve scientific, philosophical and practical problems
- appreciate the relevance of inductive logic in the current debate on the datacentric revolution in the methodology of the social sciences
Course syllabus and organization

Unique edition

Lesson period
Second semester
Course syllabus
The syllabus is shared with the following courses:
- [C72-828](
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 6
Lessons: 40 hours
Professor: Hosni Hykel