This course is structured into two modules: Network Science [netsci] and Data Visualization [dataviz], each with specific objectives and learning outcomes.

The complete course comprises both modules and is designed for students enrolled in:

• INF0007 – Analisi e Visualizzazione delle Reti Complesse (Master’s degree in Computer Science, 9 credits)

• FIS0127 – Analisi e Visualizzazione di Reti Complesse (Master’s degree in Physics of Complex Systems, 9 credits)

Students of other programs may only need to attend the [netsci] module. In particular, this applies to students enrolled in:

• MFN0954 – Reti Complesse (Master’s degree in Computer Science, 6 credits)

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Module 1: Network Science [netsci]

This module introduces the fundamental concepts, principles, and methodologies of network science, an interdisciplinary field at the intersection of computer science, physics, mathematics, and social sciences.

Students will explore:

• Graph structures and common network topologies

• Mathematical models of real-world networks

• Large-scale graph structures and community detection

• Centrality measures, including PageRank and related algorithms

• Epidemic spreading and dynamic processes on networks

• Visualization and interpretation of complex graphs

Hands-on sessions will guide students in applying Complex Network Analysis (CNA) techniques to real datasets using Python, enabling them to model, analyze, and interpret networked systems such as the Web and online social platforms.

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Module 2: Data Visualization [dataviz]

This module provides a comprehensive introduction to the principles and practices of data visualization, focusing on how to design, implement, and evaluate effective visual representations of complex data.

Key topics include:

• Principles of visual perception and design for effective communication

• Visualization of different data types: multivariate, temporal, textual, spatial, hierarchical, and network data

• Techniques for working with large-scale, real-world datasets

• Static and interactive visualization methods

Practical exercises will introduce tools and libraries for visualization, including the Python ecosystem (e.g., Matplotlib, Plotly, Seaborn) and JavaScript libraries such as D3.js for interactive, web-based visualization.