508211 - DATA SCIENCE

courses

ID:

508211

Duration (hours):

66

CFU:

SSD:

STATISTICA

Year:

2025

Date/time interval

Primo Semestre (22/09/2025 - 19/12/2025)

Course Objectives

The course is an interdisciplinary introduction to the emerging science of complex networks and their applications. Topics to be covered include the mathematics of networks (graph theory), data analysis, and applications to economics, sociology, finance, and other fields. Students will learn about the ongoing research in the field, and ultimately apply their knowledge to conduct their own analysis of a real network data set of their choosing as part of the final project. Course Objectives: -Understand the basic concepts of graph theory and complex networks. -Examine network formation models and the property of scale-free networks. -Analyze emergent properties of real networks, such as degree correlations and centrality. -Explore communities in networks and methods for identifying them. -Deepen understanding of motifs in network structure and their functional relevance.

Course Prerequisites

Algebra, especially knowledge of matrix calculus. Statistics with particular reference to discrete and continuous distributions.

Teaching Methods

Three weekly lessons of two hours each. Lectures, 2 theoretical and 1 practical on Matlab. Power point slides will be used for the theoretical lessons, while codes and datasets will be used for the practical lesson. The material will be placed at provision of students in the section dedicated to teaching on the moodle platform KIRO.

Assessment Methods

Final project presentation — complete analysis of a real network. In place of a midterm exam, there will be an intermediate presentation to check your progress and provide feedback. For the final project, students will collect data representing a real network of their choice and analyze it using the network measures and computational tools introduced in class. The goal is to craft a complete “story”: what does network science tell us about the system’s organization and function? The project is intended to mirror a real network/data science research project. For the final project, students will have to write a report (20 points) and then present the work done through slides (10 points).

Texts

Network Science 1st Edition by Albert-László Barabási (Author)

1) Introduction to Network Science Definition of networks and graphs Contextualization of complex networks in natural and artificial systems 2) Graph Theory Fundamental concepts: nodes, edges, directed and undirected graphs Mathematical representation of graphs 3) Random Networks Models of random network formation Properties of random networks and comparison with real networks 4) Scale-Free Networks Description of the Barabasi-Albert model Hubs and scale-free properties of real networks 5) Degree Correlations Definition and measurement of degree correlations Impact on the topologies of real networks 6) Centrality and Weighted Networks Centrality metrics and their interpretation Adaptation of metrics for networks with weighted edges 7) Communities Definition of communities in complex networks Algorithms for community detection 8) Motifs Concept of motif in a network Statistical analysis of motif frequency in real networks

Course Language

English

More information

NOTE: students enrolled in the Inclusive Learning Modalities programme (“Modalità didattiche inclusive) are requested to contact the Professor and the Degree Course Coordinator in order to assess specific needs and define targeted support actions.