Course Details for A.Y. 2018/2019

The information on this page refer to the academic year 2018/2019. Therefore, several links are not available and data may be incomplete. To read the current information on the course, if it is still available, go to the university course catalogue . To read information relating other academic years, use the list at the bottom of this page.

A. Year:

2018/2019

Name:

Knowledge, Language and Representation / Knowledge, Language and Representation

Type:

Course

Basic information

Code:

DT0346

Sector:

M-FIL/02

Credits:

: Master Degree in Applied Data Science 6 CFU (b)

Term:

1^st semester

Degree(s):

Master Degree in Applied Data Science 1^st anno curriculum Data for Smart City Compulsory
Master Degree in Applied Data Science 1^st anno curriculum Data for Life Science Compulsory

Language:

English

Teacher(s):

Giorgio Lando

Course Objectives

The course aims to make the students aware of the ways in which various kinds of knowledge, evidence, datum, theory, justification and induction are interpreted by contemporary epistemology. Moreover, it aims to illustrate and critically discuss how the availability of large data amounts might change the roles of data and theory in scientific research.

Course Content

The definition of knowledge and Gettier’s thought experiments.
The value of knowledge; the roles of justification and rationality in knowledge; virtue epistemology.
Perception; sense data; a priori/a posteriori distinction; induction, the old and new riddles of induction.
Passive knowledge and understanding; Google-knowing; data, information and noise; information cascades.
The alleged end of theory; does the “data deluge” make scientific method obsolete?
Data-centric biology as an epistemological case study; data, experiments and theories in data-centric biology.

Learning Outcomes (Dublin Descriptors)

On successful completion of this course, the student should

know the fundamentals of epistemology; understand why epistemological concerns are important for data scientists and data analysts; apply this knowledge and understanding to specific case studies concerning data-driven activities and disciplines.

Prerequisites and Learning Activities

No previous acquaintance with philosophy is required. A basic competence in propositional and predicate logic is useful, but not required.

Assessment Methods and Criteria

The students will be assessed on the basis of a short written essay and of an oral exam. The short essay (3000-4000 words) should concern one of the topics in the reference texts (see above). The specific topic of the essay will be determined on the basis of the student’s interests and needs, during office hours. The essay should be submitted to the teacher by email, at least fourteen days before the oral exam. The essay is expected to show that the student masters the basic terminology of contemporary epistemology and is able to argue for a thesis in a logically structured and sound way. The oral exam consists of two open questions and concerns the topics discussed during the lectures.

Textbooks

Duncan Pritchard, What Is This Thing Called Knowledge? 4th Edition , Routledge. 2018. chs 1-10, 14
Michael Lynch, The Internet of Us. Knowing More and Understanding Less , Liveright. 2016.
Sabina Leonelli, Data-Centric Biology. A Philosophical Study , University of Chicago Press. 2016.

Course page updates

This course page is available (with possible updates) also for the following academic years:

To read the current information on this course, if it is still available, go to the university course catalogue .

Course information last updated on: 30 luglio 2018, 15:54