Statistical Sciences

1. Knowledge and understanding: what students should know on the course topics after having passed the exam After passing the exam, students have got a basic knowledge on both the history and the development of network analysis as an autonomous methodology to study relational data; on the main intellectual traditions, and the most relevant scholars who have contributed to its growth in the studies on the structure and the dynamics within and between groups (Moreno, Freeman, Mit, Harvard School). Moreover, students know the main properties of a relational data matrix; some basic concepts about the nodes and the relationships (lines, directions); they are able to calculate some specific metrics (density, centrality and centralization, betweenness, closeness, clustering); to use some statistical techniques (components, core and clique), and create adequate graphs representation of social networks. 2. Applying knowledge and understanding: what students should be able to do after having passed the exam After passing the exam, students are able a) to apply theoretical schemes to complex social phenomena, traducing them in concrete research questions, smart objectives, and working hypothesis; b) students learn to gather relational data and treat them employing appropriate social network techniques, c) they show a good confidence with using Sas Viya and Ucinet software. 3. Making judgements: activities through which critical faculties should be developed. Critical capabilities are expected to be developed through the involvement of the students in active class-work sessions. Indeed, the teaching method aims at encouraging all students, individually or in group, to observe, to analyse, to critically comment, to interpret, and share ideas, in order to get through decision making, and problem solving about specific data analysis issues posed by the lecturer. 4. Communications skills and activities through which the ability to communicate what was learned is developed. The ability to communicate is developed through working group and the presentation/discussion of the results of the class activities (data analysis presentations). 5. Learning skills: ability to continue studying the topics. The competences acquired should contribute to both strengthen students’ knowledge on social networks, and improve their capabilities to learn more advanced methods and techniques of network analysis about complex social phenomena at theoretical and applied level.