Master in Computational Social and Political Science
Milan, Italy
DURATION
2 Years
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
26 Aug 2025*
EARLIEST START DATE
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TUITION FEES
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STUDY FORMAT
On-Campus
* application for matriculation: from 17/09/2025 to 24/09/2025
Introduction
The Master's Degree Programme in Computational Social and Political Science (CSPS) equips students with the knowledge and competences needed to provide empirically-grounded and theoretically-informed explanations of political and social phenomena, applying computational and quantitative methods of analysis to quantitative and qualitative data. Entirely taught in English, the program combines the hypothesis-driven deductive approach typical of the social sciences with the inductive approach of data science, enabling students to develop a robust conceptual, methodological, and practical repertoire for empirically grounded analysis of social and political phenomena.
Graduates are able to conduct projects in social and political research, with observational or experimental research designs, with the aim of testing theoretically-grounded hypotheses, exploring aggregate phenomena and trends, and developing evidence-based proposals for political and social interventions. Students work with primary survey data, digital data (including social media data), and secondary data, including numerical and textual data, to be collected, managed and analyzed using statistical or computational models, large language models, machine learning, and statistical learning techniques. By integrating attention to theory, qualitative data and factors, and advanced computational techniques, students are stimulated to develop a mindset for causal inference and fine-grained detection of generative, causal mechanisms driving complex socio-political outcomes, including collective opinions, social dynamics, and political trends.
Throughout the Programme, students receive extensive, integrated, and cutting-edge training in analytic methods, statistics, and computational science. Students are equipped with solid methodological foundations by means of a compact training on different designs for social research and policy analysis, and evaluation. The focus is on survey, experimental, and computational approaches, and will be supported by appropriate foundations in computer programming and data management, including related ethical and legal issues. Course topics include state-of-the-art techniques in multivariate analysis, machine learning, text-as-data, social network analysis and network science, causal inference, and agent-based computer simulation models. Epistemological frameworks, disciplinary theories and qualitative insights and data from the field are incorporated as the context supporting an informed use of each modelling technique.
The courses include a substantial amount of practical training, as well as individual and group project activities, closely connected with real-world data and case studies. The teaching methods aim to foster the methodological posture of computational social and political scientists, enabling students to approach the analysis of political and social phenomena starting from the formulation of relevant, empirically testable hypotheses, linking phenomena to models, designing consistent procedures for data collection and evidence mapping, and evaluating the implications of results in terms of strategic political decisions, intervention and evaluation.
The Programme requires the attainment of 84 credits from compulsory exams, including 27 credits from courses on observational and experimental designs for computational political and social research, 6 credits in computer science methods for large language models, 6 credits on ethical and legal issues related to data and computational analyses, and 45 credits on computational and statistical models for survey, digital, network, and text data. In addition, students acquire 12 credits from other additional elective and optional activities, 9 credits from internships (6 for students who need to earn 3 ECTS for Italian language A2), and 15 credits for the final thesis are provided.
Admissions
Scholarships and Funding
The University provides a range of financial benefits to students meeting special requirements (merit, financial or personal conditions, international students).
Curriculum
Year 1
Compulsory
- Advanced Multivariate Analysis
- Data Governance: Ethical and Legal Issues
- Foundations of Statistical Modelling for Social and Political Sciences
- Policy Design
- Programming for Social Data Science
- Research Design & Experimental Methods in the Social Sciences
- Survey Methods for Public Opinion Research
Optional activities and study plan rules
1 - Students must earn 6 credits/ects for Elective substantial course
Year 2
Compulsory
- Agent-Based Modelling
- Causal Inference in Social and Political Science
- Social Network Analysis
- Text Analytics and Machine Learning and Large Language Models
- Final Exam
Optional activities and study plan rules
2 - Students must earn 6 credits/ects for Elective substantial course
3 - Italian students or students with a certificate in Italian at level A2 or above must acquire 9 ECTS from an internship in the private sector, in government or public administration organisations or in academic institutions (including departmental laboratories and centres). Students who do not have a certificate in Italian at level A2 or above must acquire 3 ECTS of Italian language provided by the University Language Centre (SLAM), which reduces the ECTS for an internship to 6 ECTS.
Program Tuition Fee
Career Opportunities
The CSPS Programme trains the following professional profiles:
- Profile: Computational Social Scientist
Functions:
- design and implement data collection on social phenomena (both offline and online);
- analyse these data (or supervise and coordinate their analysis);
- interpret and synthesize the results of these analyses to describe complex social phenomena, map behavioral, attitudinal, or market trends, test theories about the causes of these phenomena and trends, and provide probabilistic forecasts;
- present the results of these activities, along with the information and insights derived from them, in textual, graphical, or audiovisual formats for public or private stakeholders.
Skills: knowledge of theories and methods for quantitative research; ability to collect and critically review relevant scientific literature; proficiency in designing research and studies, including research on groups, communities, and populations, surveys, experiments, and computer simulations; data collection skills for various types of data (numerical and textual) from online and offline sources; expertise in statistical and computational analysis of data on complex social contexts using languages such as R and Python.- design and implement data collection on social phenomena (both offline and online);
- analyse these data (or supervise and coordinate their analysis);
- interpret and synthesize the results of these analyses to describe complex social phenomena, map behavioral, attitudinal, or market trends, test theories about the causes of these phenomena and trends, and provide probabilistic forecasts;
- present the results of these activities, along with the information and insights derived from them, in textual, graphical, or audiovisual formats for public or private stakeholders.
Skills: knowledge of theories and methods for quantitative research; ability to collect and critically review relevant scientific literature; proficiency in designing research and studies, including research on groups, communities, and populations, surveys, experiments, and computer simulations; data collection skills for various types of data (numerical and textual) from online and offline sources; expertise in statistical and computational analysis of data on complex social contexts using languages such as R and Python.
Outlets: companies or organizations in the private sector (e.g., social media, human resources, corporate consulting); market research agencies; local or national public administrations and government agencies; university research institutes, public or private research centers; organizations in the non-profit sector.
- Profile: Computational Analyst for Public Policy
- Functions: design and implement systematic collections of evidence and data on political phenomena, including electoral campaigns and trends, the emergence and evolution of political movements and parties, and public opinion trends; analyse these data (or supervise and coordinate their analysis); interpret and synthesise results to describe complex political phenomena, map political and electoral trends, test theories about the causes of these phenomena and trends, or predict how such phenomena may unfold in the future.
- Skills: knowledge of theories and methods of quantitative research; ability to gather and critically review relevant scientific literature; proficiency in designing research and studies, including experimental designs, randomized controlled trials, and the analysis of texts and documentary materials using quantitative and computational techniques with languages such as R and Python; expertise in predictive electoral models, political strategy analysis, campaign design, online disinformation tracking, and analysis; statistical and computational analysis of data on complex political contexts.
- Outlets: companies or organizations in the private sector (e.g., political consulting, public opinion polling, social media), local or national public administrations or government agencies, political parties and organizations, foundations and think tanks, policy evaluation agencies, non-governmental organizations, international agencies, university research institutes, public or private research centers, or non-profit organizations.
- Functions: design and implement systematic collections of evidence and data on political phenomena, including electoral campaigns and trends, the emergence and evolution of political movements and parties, and public opinion trends; analyse these data (or supervise and coordinate their analysis); interpret and synthesise results to describe complex political phenomena, map political and electoral trends, test theories about the causes of these phenomena and trends, or predict how such phenomena may unfold in the future.
- Skills: knowledge of theories and methods of quantitative research; ability to gather and critically review relevant scientific literature; proficiency in designing research and studies, including experimental designs, randomized controlled trials, and the analysis of texts and documentary materials using quantitative and computational techniques with languages such as R and Python; expertise in predictive electoral models, political strategy analysis, campaign design, online disinformation tracking, and analysis; statistical and computational analysis of data on complex political contexts.
- Outlets: companies or organizations in the private sector (e.g., political consulting, public opinion polling, social media), local or national public administrations or government agencies, political parties and organizations, foundations and think tanks, policy evaluation agencies, non-governmental organizations, international agencies, university research institutes, public or private research centers, or non-profit organizations.