Programme

Students learn methodological elements of Social Network Analysis, combined with other analytical tools, such as Text Mining, applied to economic and business contexts. The study is not only theoretical but also focused on the practical application of these techniques through the use of specific software tools.
In the second part, the course focuses on acquiring managerial and strategic knowledge, including the study of leadership principles, team management, communication, problem-solving, and change management through the OCEAN method.
Below is a more detailed overview of the course content.

Part I: Business Analytics
- Introduction to Social Network Analysis and complex systems
- Network typologies and data collection
- Centrality measures
- Random, small-world, and scale-free networks
- Clustering and structural holes
- Network communities
- Key roles in knowledge exchange networks
- Robustness and resilience
- Introduction to diffusion models
- Introduction to big data analytics
- Honest signals
- Semantic Brand Score

Part II: Business Management
- Problem-solving (OCEAN method)
- Change management (OCEAN method)
- Leadership
- Team building

Software:
- SBS BI, Pajek and Gephi.

Both parts include case studies and practical exercises.

The instructor provides a more detailed syllabus on the learning platform.

Prerequisiti

The course has no specific prerequisites.

Core Documentation

Fronzetti Colladon, A., & Vestrelli, R. (2025). Social Network Analysis and Text Mining for Big Data: The Power of Words and Networks. Routledge.

Fronzetti Colladon, A., & Grippa, F. (2025). Leading Meaningful Change. Edward Elgar.

All the materials made available by the instructor on the learning platform are an integral part of the course and are covered in the exam. They are provided in addition to the textbooks listed here.

Attendance

Students are strongly encouraged to attend classes, as in-class participation provides the opportunity to deepen their understanding through direct interaction with the instructor and classmates. Studying the materials provided and recommended by the instructor is important, as is attending lectures, which allows students to gain additional perspectives and information. Students who are unable to attend for valid reasons, as specified by the University regulations – such as working students – are invited to contact the instructor for further guidance.

Type of evaluation

Learning is assessed through the evaluation of a project carried out in groups, usually of 4 or 5 students, and submitted as a report, and an oral examination covering all course topics. The report is graded out of 30 before or at the start of the oral examination, and this grade serves as the starting grade for the oral examination. The overall assessment will take into account the student’s knowledge and understanding of the topics covered in the course, their ability to discuss them, and the quality of the report. Detailed information on the group work and further information on the examination procedures are available on the learning platform.

Programme

Students learn methodological elements of Social Network Analysis, combined with other analytical tools, such as Text Mining, applied to economic and business contexts. The study is not only theoretical but also focused on the practical application of these techniques through the use of specific software tools.
In the second part, the course focuses on acquiring managerial and strategic knowledge, including the study of leadership principles, team management, communication, problem-solving, and change management through the OCEAN method.
Below is a more detailed overview of the course content.

Part I: Business Analytics
- Introduction to Social Network Analysis and complex systems
- Network typologies and data collection
- Centrality measures
- Random, small-world, and scale-free networks
- Clustering and structural holes
- Network communities
- Key roles in knowledge exchange networks
- Robustness and resilience
- Introduction to diffusion models
- Introduction to big data analytics
- Honest signals
- Semantic Brand Score

Part II: Business Management
- Problem-solving (OCEAN method)
- Change management (OCEAN method)
- Leadership
- Team building

Software:
- SBS BI, Pajek and Gephi.

Both parts include case studies and practical exercises.

The instructor provides a more detailed syllabus on the learning platform.

Prerequisiti

The course has no specific prerequisites.

Core Documentation

Fronzetti Colladon, A., & Vestrelli, R. (2025). Social Network Analysis and Text Mining for Big Data: The Power of Words and Networks. Routledge.

Fronzetti Colladon, A., & Grippa, F. (2025). Leading Meaningful Change. Edward Elgar.

All the materials made available by the instructor on the learning platform are an integral part of the course and are covered in the exam. They are provided in addition to the textbooks listed here.

Reference Bibliography

Other recommended books: - Brown, T. Change by Design, Revised and Updated. Harper Business. - Verganti, R. Overcrowded: Designing Meaningful Products in a World Awash with Ideas. The MIT Press.

Attendance

Students are strongly encouraged to attend classes, as in-class participation provides the opportunity to deepen their understanding through direct interaction with the instructor and classmates. Studying the materials provided and recommended by the instructor is important, as is attending lectures, which allows students to gain additional perspectives and information. Students who are unable to attend for valid reasons, as specified by the University regulations – such as working students – are invited to contact the instructor for further guidance.

Type of evaluation

Learning is assessed through the evaluation of a project carried out in groups, usually of 4 or 5 students, and submitted as a report, and an oral examination covering all course topics. The report is graded out of 30 before or at the start of the oral examination, and this grade serves as the starting grade for the oral examination. The overall assessment will take into account the student’s knowledge and understanding of the topics covered in the course, their ability to discuss them, and the quality of the report. Detailed information on the group work and further information on the examination procedures are available on the learning platform.