The Epistemology of Big Data
A.Y. 2024/2025
Learning objectives
The course aims at developing the logico-mathematical background to assess critically the logic and episte-mology of inductive reasoning, or "reasoning with data". In addition to making students familiar with the rel-evant elementary logical, probabilistic and statistical notions, it focusses on how the formalisation of induc-tive inference sheds crucial methodological light on the "datacentric" revolution, which is currently dotting the development of the natural and social sciences.
Expected learning outcomes
Students are expected to acquire a full understanding of the formal notions presented and master basic knowledge of the following topics:
- know the central concepts and reasoning tools of discrete mathematics
- know the central concepts in elementary probability theory
- know how to apply elementary logic to formalize probabilistic concepts
- 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"
- know the central concepts and reasoning tools of discrete mathematics
- know the central concepts in elementary probability theory
- know how to apply elementary logic to formalize probabilistic concepts
- 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"
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 activities: 50% of the final mark
- End-of-course project: 50% of the final mark
- Online questions-based exam / flipped classroom activities: 50% of the final mark
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 6
Lessons: 48 hours
Professor:
Hosni Hykel
Educational website(s)
Professor(s)
Reception:
Friday 8:30-11:30
Second Floor, Cortile Ghiacchiaia. Please email me to secure your slot.