Catalogo dei Corsi di studio

Data science is pivotal for many social-relevant issues (information diffusion, mobility, etc.) and is often requested to support policymaking. 

The course aims to design efficient strategies to extract knowledge from data through the complex systems approach.

The course will introduce advanced topics of network science and diffusion models and address the data-driven modeling of complex socio-technical systems (e.g., misinformation diffusion, echo chambers, bot detection, mobility patterns, and resilience).

The first part of the course will explore the foundational aspects of advanced topics of complex networks (multilayer networks, percolation, 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 up to patterns of human mobility, passing through information operations, and bot detection.

Introduction

-       Introduction to the course 

-       A complete example of data-driven modeling of complex systems: the case of misinformation diffusion 

Advanced Concepts in Complex Systems

-       Recall of Network Science concepts ( stochastic network models and metrics) 

-       Implementation of the Small world effect (from the watts strogatz paper)

-       Preferential Attachment and other Generative models

-       Multilayer networks

-       Dynamics Networks

Networks from Data and Processes 

-       Ethical Issues and Accessing social data online (Facebook, Twitter, Youtube, Reddit)  

-       Spreading processes on different type of networks

-       Voter Model on different type of networks

-       Bounded Confidence Model on different types of networks 
Introduction to Percolation Theory

Case Studies

Modeling Misinformation

-       The spreading of misinformation online (using Facebook data) 

Modeling Cyber threats: Information and psychological operations

• Information operations and the detection of Social Bots 

Self-healing networks

- Modeling resilient of complex systems

Human Mobility

-       Human mobility during the pandemic (using Facebook Data) 

Modeling the interplay between social media algorithms and social dynamics

-       Capturing the polarizing effect of feed algorithms (comparing social dynamics on different social media)

Modeling Memes as a language

-       Unraveling the evolution of memes complexity

Course Objective: Thi course provides an in-depth understanding of the interdisciplinary field of Data Science and its application in understanding complex phenomena. The course introduces fundamental principles of data science, computer science, and network theory, allowing students to apply these concepts practically through hands-on projects.

Course Learning Objectives: By the end of the course, students will be able to:

• Understand data science principles and how it applies to complex systems.
• Understand and analyze complex networks using concepts such as random networks and scale-free networks.
• Grasp fundamental computer science concepts, including computer architectures, algorithms, and databases.
• Use R programming language for data manipulation, visualization, and analysis.
• Apply basic and advanced analytic techniques, including segmentation, clustering, classification, bivariate, and sentiment analysis.
• Understand and apply network analytics using iGraph.
• Complete a practical project under the guidance and mentoring of the course instructor, demonstrating the application of the learned concepts.

Detailed Program: The course is strategically structured into six units:

1. General Overview: Introduction to key concepts, including social media mining and graph theory.
2. Complex Networks: Exploration of complex networks such as random and scale-free networks.
3. Basic Computer Science: Deep dive into computer architectures, algorithms, and databases.
4. Basics of R: Comprehensive learning of R for data manipulation, visualization, and analysis.
5. Basic and Advanced Analytics: Hands-on experience with essential data science techniques and understanding privacy issues related to data.
6. Network Analytics: Practical network analysis using iGraph.
7. Project Mentoring: Application of learned concepts in a practical project with guidance from the instructor.

Course Objective: This course aims to equip students with the skills necessary to analyze large volumes of data from various sources using data analysis and mining techniques. The course will cover the main stages of an analysis pipeline, from data collection and cleaning to transformation, modeling, and forecasting.

The course will also tackle ethical and privacy issues associated with handling personal and sensitive data in the new digital context. Furthermore, the course will focus on fundamental data analysis techniques, such as clustering, classification, and data segmentation, and their effective visualization.

Finally, the course will introduce the basic network science concepts and metrics for analyzing complex networks.

Course Learning Objectives: By the end of the course, students will be able to:

• Understand the principles and methodologies of data analysis and data mining.
• Design and implement an efficient pipeline for analyzing and visualizing large volumes of data from various sources.
• Use the specific languages Python and R to perform segmentation, clustering, and classification operations on data, applying the most advanced and appropriate techniques to the case study.
• Effectively visualize data, choosing the most suitable tools and charts for the type and size of the data.
• Use and manipulate the basic concepts of network science and the derived metrics to analyze complex networks.

Detailed Program:

• Course Introduction
• Introduction to the issues of analyzing large volumes of data
• Overview of Complex Systems
• Introduction to specific languages for data analysis (R - Python)
• Segmentation
• Clustering
• Classification
• Introduction to Random and Scale-Free Networks
• Network Centrality Metrics
• Algorithms for Community Partitioning
• Pipelines for Data Analysis
• Features Extraction
• Data Gathering
• Data Modeling
• Data Visualization