Workshop: team management

A.A. 2024/2025
3
Crediti massimi
36
Ore totali
Lingua
Inglese
Obiettivi formativi
The course aims to provide knowledge on utilizing network science and data analysis as tools for effective team management, enabling students to understand and optimize team dynamics, collaboration patterns, and organizational structures from a data science point of view.
Risultati apprendimento attesi
Students will develop an understanding of fundamental social network analysis concepts, gain practical skills in analyzing team communication patterns, and learn to identify key organizational structures through network metrics.
Corso singolo

Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.

Programma e organizzazione didattica

Edizione unica

Periodo
Secondo semestre

Programma
- Introduction to the course
- Bipartite networks
- Density and degree
- Connected components
- Centrality
- Clustering coefficient & Transitivity
- Bridges
- Reciprocity
- Assortativity
- The nature of social structure: Milgrim, Granovetter and Dunbar theories
- Community detection

link per online:
https://itucph.zoom.us/j/69137603315
Prerequisiti
Suggested: Programming in Python.
Metodi didattici
Educational activities in computer laboratory.
Materiale di riferimento
Slides of the lessons, posted after each class.
Reza Zafarani, Mohammad Ali Abbasi, Huan Liu. Social Media Mining: An Introduction. A Textbook by Cambridge University Press http://www.socialmediamining.info/
Modalità di verifica dell’apprendimento e criteri di valutazione
Group project on network analysis
Final evaluation will consist of an in-person oral examination where each project group will present their work. During the presentation, students must:
- Demonstrate their project outcomes
- Explain their collaboration process, including: Tools and platforms used for team coordination; Individual responsibilities and contributions; Areas of collective discussion and group decision-making
The examination will be graded on a pass/fail basis, with possible outcomes of either 'Approved' or 'Not Approved.'
- CFU: 3
Laboratori Umanistici: 36 ore
Docente: Galdeman Alessia