This course comprises two modules, namely, [netsci] and [dataviz], with the objectives described below.

The full version of the course includes both modules. This is of interest to students of INF0007 "Analisi e Visualizzazione delle Reti Complesse" (Master's degree in Computer Science, 9 credits) and FIS0127 "Analisi e Visualizzazione di Reti Complesse" (Master's degree in Fisica dei Sistemi Complessi, 9 credits).

Other students may have to consider only the [netsci] module. This applies, in particular, to students attending MFN0954 "Reti Complesse" (Master's degree in Computer Science, 6 credits).

Network Science [netsci]

This module introduces the fundamental concepts, principles, and methods in the interdisciplinary field of network science, with a particular focus on analysis techniques, modeling, and applications for the World Wide Web and online social media. Topics covered include graphic structures of networks, mathematical models of networks, common network topologies, the structure of large-scale graphs, community structures, epidemic spreading, PageRank and other centrality measures, dynamic processes in networks, and graphs visualization. Additionally, students will learn how to apply the basic principles of network science to perform CNA (Complex Network Analysis) tasks on real data with R and/or Python and many different packages/libraries such as NetworkX, iGraph, networks, and so on, as well as advanced graph visualization tools as GePhi.

Data visualization [dataviz]

This module will cover an overview of the main principles of designing and evaluating effective data visualizations. Students will learn how to deal with real-world, large-scale datasets and several data types, gaining confidence in visualizing multivariate, temporal, textual, spatial, hierarchical, and network data. Practical sessions will show how to implement static and interactive visualizations using the Python library ecosystems, Javascript web libraries like D3.js for dealing with interactivity, and widespread standalone tools such as Gephi.