Data Science

General This course aims to exploit advanced techniques from network science and complex systems to understand and eventually predict social-relevant issues (information diffusion, mobility, etc.). The course aims to design efficient strategies to extract knowledge from data through the complex systems approach by stressing the combination of network science and complex systems to build sound mathematical models of complex phenomena. The course will introduce advanced topics of networks science and diffusion models and address the data-driven modeling of complex socio-technical systems (e.g., misinformation diffusion, echo chambers formation, bot detection, mobility patterns, system resilience). The first part of the course will explore the foundational aspects of advanced topics of complex networks (multilayer networks, percolation theory, time-varying graphs). The second part will apply those concepts to actual cases from up-to-date scientific findings ranging from the effect of feed algorithms on social dynamics to patterns of human mobility, passing through information operations, and bot detection. We will use data from real case scenarios (from Facebook, Twitter, Mobility Data, etc.) to analyze phenomena and build and validate models of complex phenomena. Specific • Knowledge and understanding: To know and discuss recent advances in the area of data-driven modeling of complex systems, in particular on algorithms and models to understand and eventually predict social dynamics (e.g., information diffusion, polarization) • Applying knowledge and understanding: to know how to apply criteria and techniques for designing a data analysis framework exploiting the theory of complex systems. • Making judgments: to select the most appropriate strategy to cope with the data-driven modeling of complex phenomena • Communication skills: know how to present projects, including design constraints, solutions, and use possibilities. • Learning skills: ability to develop more advanced studies in data-driven modeling of complex systems.