First Year

Module	CFU	Description
Fundamental of social science research design	6	Provides a basic knowledge of social research design
Element of statistics, probabilistic reasoning, and R	6	Provides elements of statistics and probabilities. Use of R programming language
Computer programming (Python)	6	Introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python
Digital Sociology	6	The course introduces the student to sociology with a special focus on digital sociology (e.g.: digital communication and interaction, social media and society…)
Notions of Database Management Systems	4	The course introduces the student to DBMS, that provides methods to create, manage, and access a large volume of data
Basic Social Network Analysis	2	This course provides an introduction to the basic principles and classic themes dominating theoretical work in the social network field. Students will learn social networks theory as well as how to do network analysis
Linguistic theories and computational linguistics	6	Introduces students to the development of formal theories of grammar and semantics, focusing on the practical outcome of modelling human language
Logic and causal reasoning	2	This course provides students with the basics of logic reasoning, the challenge of causal reasoning for artificial intelligence systems
Non-relational Database Management Systems	4	Introduction to NoSQL designed to handle large volumes of unstructured data, such as social media feeds, sensor data, that traditional DB may not be able to handle efficiently
Artificial Intelligence Introduction	4	Introduction to BD and Artificial Intelligence, history, algorithms, supervised vs unsupervised learning, classification
AI Algorithms (Machine Learning, Natural Language Processing…)	6	This module introduces ML, NLP, and Generative Artificial Intelligence capable of generating new content based on patterns and rules learned from existing data (e.g.: chatGPT)
AI Algorithms for Social Science	4	Uses relevant examples from social science research to cover major ML tasks including regression, classification, clustering, and dimensionality reduction
Seminar	4	

Second Year

Module	CFU	Description
Techno-social Data Theory	4	This course introduces students to social, political, economic and philosophical dimensions of data, AI, and ML. Through a combination of lectures, seminars and exercises, this course will provide innovative socio-technical methods for addressing their societal impacts
Data Visualization	4	This course provides an overview of the latest logic and tools in data visualization, including best practices for visualizing graph and statistics for predictive analytics
Law, ethics and data governance	6	This course introduces students to key legislation and the political and ethical debates regarding data governance and security
Data, Power and Society	4	This course will explore the power dynamics of data inequality, that is, the unequal access to and control over data, and its highly uneven impacts on society
Seminar	2	
BD and AI: Social and Evaluation Research	6	Through a combination of lectures, seminars and exercises, this course shows how classic social and evaluation research issues can be addressed by using BD & AI models
BD analytics and Social Network Analysis	4	This course deals with the analysis of complex networks, made possible by the availability of BD, with a special focus on the social network and its structure.
Digital methods	6	This course focuses on the digital media contexts where data is generated as a by-product of social interaction and on new ways of combining quantitative and qualitative methods of digital inquiry and analysis
Seminar	4	
Stage	6	The internship activity outlined in the educational plan of the master’s program is organized under the responsibility of the Scientific Committee, in collaboration with the hosting organizations, taking into account the interests of the students. For students who are already employed, there is the possibility of carrying out a Project Work activity within the context of their current job, aimed at actively experimenting with the knowledge acquired during the educational path of the master’s program. This activity is defined in agreement between the Scientific Committee, the employer, and the student.
Thesis	14