Social Data Science
Milano
Master 2 Livello
Inglese
24 mesi
blend-mista
Vision
Social scientists are progressively gaining access to large and complex data sets. Technological innovations have made the collection of that data - by private and public sectors – an everyday norm. Advances in computer science, statistics, and artificial intelligence enable us to perform sophisticated real-time analyses in several fields, which are increasingly in demand in the job market.
The Master's course "Social Data Science" is funded by the Project FOSSR - Fostering Open Science in Social Science Research with NRRP funds, NextGenerationEU.
Mission
A multidisciplinary master’s degree that equips students with state-of-the-art social and technical expertise to analyse and interpret social data and generate insights on human behaviour and society.
For student new to computational methods, this is a chance to develop competencies already in demand in the job market, learning state-of-the-art skills so they can thrive in a changing, data-driven workplace.
This is also an opportunity for students new to the social sciences to see where computational and statistical skills can go with innovative applications to issues of great societal concern. The multidisciplinary content of the master's program will empower students to better address societal challenges in the digital age.
The program offers e-learning with real-time interaction with instructors and the opportunity to access supplementary materials through a virtual platform.
Organizzazione Didattica
2500 ore di Lezione
500 ore di Stage/Project Work
Esami e Frequenza
Insegnamenti
- Provides a basic knowledge of social research design.
- Provides elements of statistics and probabilities
- Use of R programming language
- Introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python.
- The course introduces the student to sociology with a special focus on digital sociology (e.g.: digital communication and interaction, social media and society…)
- The course introduces the student to DBMS, that provides methods to create, manage, and access a large volume of data
- 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
- Introduces students to the development of formal theories of grammar and semantics, focusing on the practical outcome of modeling human language.
- This course provides students with the basics of logic reasoning, the challenge of causal reasoning for artificial intelligence systems
- 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.
- Introduction to BD and Artificial Intelligence, history, algorithms, supervised vs unsupervised learning, classification
- 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)
- Uses relevant examples from social science research to cover major ML tasks including regression, classification, clustering
- 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-techncal methods for addressing their societal impacts.
- 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
- This course introduces students to key legislation and the political and ethical debates regarding data governance and security
- 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
- 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
- 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
- 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
Comitato Scientifico
Bennato
Coté
Quali competenze acquisisci ?
Social Data Science
A chi è rivolto ?
Sociology and Social Research, Statistics, Computer Science, and Economics.
Sociology and Social Research, Statistics, Computer Science, and Economics.
The course is designed for graduates in Sociology and Social Research, Statistics, Computer Science, and Economics. Selection is based on their curriculum vitae and an interview where, in addition to motivation, their knowledge of some basic topics in social research methodology and statistics will be evaluated.
Sbocchi professionali
Digital Sociology
The program provides students with a comprehensive introduction to digital sociology. Dive into the dynamic landscapes of digital communication, interaction, and the profound impact of social media on contemporary society.
Machine Learning
Dive into the world of machine learning, where you'll learn to use algorithms that enable computers to predict human behavior and patterns.
Artificial Intelligence
Study the broader field of artificial intelligence, encompassing topics like natural language processing, computer vision, and decision-making algorithms. AI plays a pivotal role in automating tasks and gaining insights from social data.
Data, Power, and Society
Learn the intricate power dynamics surrounding data inequality. Explore the profound consequences of unequal access to and control over data, which leaves an indelible imprint on the fabric of our society.