The information society: the epistemology of big data

A.Y. 2021/2022
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

Single session

Lesson period
Second semester
During the ongoing COVID emergency, the course syllabus will be maintained with the following changes made to enhance the effectiveness of the online version of the course, which was originally designed for face-to-face teaching.
Online environments used:
MSTeams: code available on Ariel.

Teaching methods:
Classes will be held according to the following risk scenarios:
- maximum severity (red zone): classes will be held only remotely in synchronous mode (using MSTeams)
- high severity (orange zone): lessons will be held in mixed mode, partly in person and partly online. the face-to-face lessons will allow the participation of students connected with MSTeams as well as students in the classroom. Online lessons will be held synchronously (using MSTeams)
- severity (yellow zone): classes will be held according with the orange zone guidelines and, if conditions allow, the number of lessons on campus will be increased.
The calendar of in person lessons and updates will be published on the online course platform.

Learning assessment procedures and evaluation criteria:
The exam is written and is held on in any emergency situation, whether yellow, orange or red zone, in compliance with the guidelines provided by the University.
Students wishing to participate in face-to-face lessons must refer to the following University provisions:…
Students wishing to participate in MSTeams lessons must refer to the following technical guides:…
To participate in the exam sessions, students must refer to the following provisions:…
Course syllabus
MODULE 1 (3 CFU) Reasoning with data

- Data and its meaning
- Elementary descriptive statistics
- Elementary probability

MODULE 2 (3 CFU) The epistemology of reasoning with data: induction

- Hume's problem of induction
- Induction and knowledge
- More data vs better data
Prerequisites for admission
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
Lecture notes and material will be provided by the instructor
Assessment methods and Criteria
The exam is oral, but it is based on a written project (see details below) which is worth 50% of the final assessment.

During the exam you will be asked to recount your project and questions will be aimed as assessing both your knowledge of the chosen topic and of the key concepts introduced throughout.
The course features in flipped classrooms. Active participation to them will be worth up to 25\% of the final mark. Flipped classrooms are not compulsory for attending students. Precise details are provided in the introductory lecture.

Practical details on Projects: 5 pages, standard LaTeX, which summarises and comments on one item from the projects list which will be made available at the end of the course. Projects are mandatory for both attending and non attending students.
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE - University credits: 6
Lessons: 40 hours
Professor: Hosni Hykel
Monday, Tuesday Wednesday 1pm-2pm
Teams, by email appointment