The Information Society: the Epistemology of Big Data
A.Y. 2024/2025
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
- 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
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
1. Reasoning with data
- Data and its meaning
- Elementary descriptive statistics
- Elementary probability
2. The epistemology of reasoning with data: induction
- Induction and knowledge
- More data vs better data
- Data and its meaning
- Elementary descriptive statistics
- Elementary probability
2. The epistemology of reasoning with data: induction
- Induction and knowledge
- More data vs better data
Prerequisites for admission
None
Teaching methods
Frontal and flipped lectures and assignments. The approach will be problem-oriented and students will be trained to learn by solving basic problems and exercises
Teaching Resources
Assessment methods and Criteria
The exam is written, it is taken online, and it is marked as follows:
- End-of-course project: 50% of the final mark
- Online questions-based exam/flipped classroom activity: 50% of the final mark
- End-of-course project: 50% of the final mark
- Online questions-based exam/flipped classroom activity: 50% of the final mark
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 6
Lessons: 40 hours
Professor:
Hosni Hykel
Shifts:
Turno
Professor:
Hosni HykelEducational website(s)
Professor(s)
Reception:
Friday 8:30-11:30
Second Floor, Cortile Ghiacchiaia. Please email me to secure your slot.