Data Science for Economics and Health
Data Science for Economics and Health
Course sheet
A.Y. 2025/2026
Master programme
LM Data - Data science
The master's degree course in Data Science for Economics and Health (DSEH), entirely delivered in English, aims to provide advanced education on methodological methods and tools in computer science, statistics, and mathematics designed to interpret and analyze complex phenomena in the fields of economics and health. The course of study offers advanced skills through the study of emerging information technologies about data management and scalability of analysis systems in cloud environments, advanced statistical and mathematical techniques, as well as machine learning techniques for information extraction and classification. Furthermore, the course addresses topics about economic theory, decision theory under conditions of uncertainty, econometrics, and time-series analysis, biostatistics and epidemiology. Graduates of the DSEH MSc program will receive advanced education on methodologies and tools in computer science, quantitative and methodological notions to interpret and analyze economic phenomena using approaches that integrate business, market and social media data. Among these, the MSc program focuses on the analysis of the effects of economic policies as well as the evaluation of actions and any other activity related to the sectors of economy, environment, marketing and business. Moreover, the MSc program aim to provide the foundations of epidemiology and biostatistics on which to graft the acquired knowledge of data analysis. The DSEH course bolsters the construction of solid methodological bases by addressing topics of the economic theory, decision theory under uncertainty conditions, micro-econometric techniques and time-series analysis. It also fosters the study of emerging data management technologies and scalability of analysis systems in cloud environments, as well as machine learning techniques for the extraction and classification of information.
In addition to these compulsory activities, the DSEH course allows students to autonomously customize/specialize the study plan according to their own inclinations, by choosing elective courses up to 18 ECTS in total between three different educational paths, namely "Data Science" path, "Economic Data Analysis" path, and "Health" path. A first kind of specialization focus is about the aspects of methodological and technological innovation, advanced statistical methods, techniques of social media analysis and textual analysis as well as their impact on the data-driven business. A further kind of specialization offers useful tools for economic applications in policy or investment assessment, the study of production processes, and the evolution of social phenomena, with a focus on environmental issues. Finally, the third specialization is devoted to the analysis of medical data and the study of the relationship between exposure and health in the population and to provide the tools to critically evaluate the epidemiological literature.
These specialization activities are geared, together with the external training activities, to the preparation of the dissertation and to the final exam. Therefore, the dissertation is considered as the fulfilment of the course of study and the learning process began with the choice of the educational path.
The courses of DSEH, both compulsory and elective, include lectures and laboratory classes as well as autonomous project activities and individual activities to guarantee an adequate preparation also from a practical point of view, in close contact with case studies and real data.
The in-depth studies in mathematics, statistics, computer science and economics highly qualify the educational project of Data Science for Economics and Health, and they also pave the way to students interested in PhD and research programs in the areas of Data Science, Computer Science, Economics, and Epidemiology and Public Health.
In addition to these compulsory activities, the DSEH course allows students to autonomously customize/specialize the study plan according to their own inclinations, by choosing elective courses up to 18 ECTS in total between three different educational paths, namely "Data Science" path, "Economic Data Analysis" path, and "Health" path. A first kind of specialization focus is about the aspects of methodological and technological innovation, advanced statistical methods, techniques of social media analysis and textual analysis as well as their impact on the data-driven business. A further kind of specialization offers useful tools for economic applications in policy or investment assessment, the study of production processes, and the evolution of social phenomena, with a focus on environmental issues. Finally, the third specialization is devoted to the analysis of medical data and the study of the relationship between exposure and health in the population and to provide the tools to critically evaluate the epidemiological literature.
These specialization activities are geared, together with the external training activities, to the preparation of the dissertation and to the final exam. Therefore, the dissertation is considered as the fulfilment of the course of study and the learning process began with the choice of the educational path.
The courses of DSEH, both compulsory and elective, include lectures and laboratory classes as well as autonomous project activities and individual activities to guarantee an adequate preparation also from a practical point of view, in close contact with case studies and real data.
The in-depth studies in mathematics, statistics, computer science and economics highly qualify the educational project of Data Science for Economics and Health, and they also pave the way to students interested in PhD and research programs in the areas of Data Science, Computer Science, Economics, and Epidemiology and Public Health.
The MSc program in Data Science for Economics and Health aims to train the following professional figures:
Profile: Data Scientist
Functions: its main functions are i) to analyze and elaborate forecasts on large data flows, ii) to identify and apply the most suitable software tools and statistical techniques for their processing, iii) to create complex models for predictive data- based analysis. The Data Scientist knows the different contexts in which data emerge and she/he knows how to interact with experts from various disciplines.
Skills: statistical analysis, programming, knowledge of software tools.
Outlets: large companies, small and medium-sized enterprises, startups and Public Administration. They can work in manufacturing, telco and media, services, banking-insurance, utilities sectors.
Profile: Data Analyst
Functions: its main functions are the identification and supervision of operational decision-making processes in direct coordination with the company executive management. They can work in marketing, business, management innovation, and finance.
Skills: baggage of theoretical knowledge about economics, statistics and computer science to support both organizational and development decisions of economic institutions and companies.
Outlets: large companies, small and medium-sized enterprises and consulting firms operating in various sectors such as manufacturing, telco and media, services, banking-insurance, utilities.
Profile: Data Driven Economist
Functions: its main functions are to frame problems of economic analysis in the context of data science by identifying data and technologies capable of providing new keys to interpret or to evaluate economic and social phenomena.
Skills: economic theory, statistical, econometric and computer science techniques. Outlets: large companies, Public Administration and international organizations.
Profile: Data-Driven Decision Maker
Functions: the professions included in this category perform managerial functions of high responsibility in private and public companies with an international vocation and a strong technological component, using data analysis to guide strategic and operational decisions.
Skills: wealth of theoretical knowledge about economics, statistics and computer science to support organizational and development decisions of economic institutions and companies.
Outlets: small and medium enterprises, large companies, Public Administration.
Profile: Analyst of development projects or economic policies
Functions: the professions included in this category contribute to the formulation, monitoring and analysis of development projects or economic policies.
Skills: baggage of theoretical and operational notions in the field of economics, business management strategy, and the economic policies that govern them.
Outlets: they work in private or public companies in industry, commerce, business services, personal services, and companies of similar kind as well as international and/or governmental institutions.
Profile: Health Analyst
Functions: its main functions are to define the most appropriate study type modalities to answer questions related to the relationship between exposure and health in the population, propose the most appropriate statistical, computational and data management methods for experimental and observational studies.
Skills: theoretical knowledge of medical statistics and epidemiology, statistical, econometric and computer science techniques.
Outlets: Health care companies, hospitals, teaching hospitals.
Profile: Data Scientist
Functions: its main functions are i) to analyze and elaborate forecasts on large data flows, ii) to identify and apply the most suitable software tools and statistical techniques for their processing, iii) to create complex models for predictive data- based analysis. The Data Scientist knows the different contexts in which data emerge and she/he knows how to interact with experts from various disciplines.
Skills: statistical analysis, programming, knowledge of software tools.
Outlets: large companies, small and medium-sized enterprises, startups and Public Administration. They can work in manufacturing, telco and media, services, banking-insurance, utilities sectors.
Profile: Data Analyst
Functions: its main functions are the identification and supervision of operational decision-making processes in direct coordination with the company executive management. They can work in marketing, business, management innovation, and finance.
Skills: baggage of theoretical knowledge about economics, statistics and computer science to support both organizational and development decisions of economic institutions and companies.
Outlets: large companies, small and medium-sized enterprises and consulting firms operating in various sectors such as manufacturing, telco and media, services, banking-insurance, utilities.
Profile: Data Driven Economist
Functions: its main functions are to frame problems of economic analysis in the context of data science by identifying data and technologies capable of providing new keys to interpret or to evaluate economic and social phenomena.
Skills: economic theory, statistical, econometric and computer science techniques. Outlets: large companies, Public Administration and international organizations.
Profile: Data-Driven Decision Maker
Functions: the professions included in this category perform managerial functions of high responsibility in private and public companies with an international vocation and a strong technological component, using data analysis to guide strategic and operational decisions.
Skills: wealth of theoretical knowledge about economics, statistics and computer science to support organizational and development decisions of economic institutions and companies.
Outlets: small and medium enterprises, large companies, Public Administration.
Profile: Analyst of development projects or economic policies
Functions: the professions included in this category contribute to the formulation, monitoring and analysis of development projects or economic policies.
Skills: baggage of theoretical and operational notions in the field of economics, business management strategy, and the economic policies that govern them.
Outlets: they work in private or public companies in industry, commerce, business services, personal services, and companies of similar kind as well as international and/or governmental institutions.
Profile: Health Analyst
Functions: its main functions are to define the most appropriate study type modalities to answer questions related to the relationship between exposure and health in the population, propose the most appropriate statistical, computational and data management methods for experimental and observational studies.
Skills: theoretical knowledge of medical statistics and epidemiology, statistical, econometric and computer science techniques.
Outlets: Health care companies, hospitals, teaching hospitals.
The education program can be enriched by educational activities abroad both to deepen some topics and as socialization experience in international environments. Within the Erasmus+ program study periods can be taken in over 50 universities in Belgium, Czech Republic, Finland, France, Germany, Greece, Hungary, Lithuania, Norway, Netherlands, Poland, Portugal, Romania, Slovenia, Spain, Switzerland, Turkey. Courses will be recognized in the personalized study plan. These periods abroad are typically 5-month long and include courses for about 30 CFU, in the area of information and communication technology and related applications. Recognition of these educational activities will be based on the Learning Agreement, to be defined in advance by the student and the Erasmus coordinator at the Computer Science Department before starting the period abroad: course in the learning agreement with passed exams will replace the educational activities of the study plan ("manifesto"), either by covering the same topics or complementing the acquired basic competences. The Erasmus Committee at the Computer Science Department will perform the recognition of CFU obtained abroad and the definition of the personalized study plan. Similarly, stages to prepare the final dissertation are allowed in the same foreign universities. Recognition will be performed by the Department Erasmus Committee.
Erasmus: the coordinator for the Department of Informatics is Prof. Fabio Scotti.
International Programs: the coordinator for the Department of Informatics is Prof. Davide Rocchesso.
More information are available at the following link: https://di.unimi.it/it/rapporti-internazionali/mobilita-internazionale/opportunita-internazionali
Erasmus: the coordinator for the Department of Informatics is Prof. Fabio Scotti.
International Programs: the coordinator for the Department of Informatics is Prof. Davide Rocchesso.
More information are available at the following link: https://di.unimi.it/it/rapporti-internazionali/mobilita-internazionale/opportunita-internazionali
No obligation, but strongly recommended.
Enrolment
1. Curricular requirements
Candidates for admission to the master's degree course may come from various bachelor's, but must have earned at least 30 ECTS in computer science and mathematics (scientific disciplinary sectors: from MAT-01 to MAT-09, INF-01, ING- INF/05) and/or in the area of economic sciences and statistics (scientific disciplinary sectors: SECS-S/01, SECS-S/02, SECS-S/03, SECS-S/06, SECS-P/05, SECS-P/01, SECS-P/02, SECS-P/03, SECS-P/07, SECS-P/08, SECS-P/10), and/or in medical sciences (scientific sector MED/01 only).
Curricular requirements must be met by the date of effective submission of the application for admission. Students with a foreign qualification are required to provide an Italian qualification to show that they satisfy the minimum curricular requirements of DSEH.
2. Proficiency in English
Proficiency in English at a B2 level or higher per the Common European Framework of Reference for Languages (CEFR) is required for admission.
The B2-level requirement will be ascertained by the University Language Centre (SLAM) upon admission as follows:
- Language certificate of B2 or higher level issued no more than three years before the date of admission application. You will find the list of language certificates recognized by the University at: https://www.unimi.it/en/node/39322. The certificate must be uploaded when submitting the online application;
- English level achieved during a University of Milan degree programme and certified by the University Language Centre (SLAM) no more than four years before the date of admission application, including levels based on language certificates submitted by the applicant during their Bachelor's degree at the University of Milan. In this case the process is automatic, the applicant does not have to attach any certificates to the application;
- Entry test administrated by the University Language Centre (SLAM) according to the calendar published on the website: (https://www.unimi.it/en/node/39267/)
All those who fail to submit a valid certificate or do not meet the required proficiency level will be instructed during the admission procedure to take the Entry test.
Applicants who do not take or pass the Entry test will be required to obtain a language proficiency certificate recognized by the University (see https://www.unimi.it/en/node/39322) and deliver it to the SLAM via the InformaStudenti service by the deadline fixed for the master's programme (https://www.unimi.it/en/node/39267/).
Applicants who do not meet the requirement by said deadline will not be admitted to the master's degree programme and may not sit any further tests.
3. Personal competencies and skills: assessment criteria
Satisfying minimum curricular requirements is a necessary but not sufficient condition for admission. An Admission Board appointed by the Faculty Board (a.k.a. Collegio Didattico) must evaluate and manage the admission procedures of candidate students.
Assessment of personal competencies and skills of applicants is enforced through a written online admission test in English. An admission threshold is set for the test by the Admission Board, and applicants must obtain a result over the threshold for passing the test. Applicants who do not participate or obtain a result over the threshold are not admitted to the master's degree programme and are not allowed to participate in any further test. Further information about the test and the related organization are published on the degree course website when the call for admissions is opened.
For applicants who meet the curricular requirements and obtain a result over the threshold in the admission test, the Admission Board assesses the personal competencies and skills of students based on the academic curriculum (quality of the previous degree as well as the average grade obtained in the bachelor program; grades obtained in mathematics, statistics, computer science and economics courses are part of the evaluation) and choice coherence (coherence between the academic curriculum and/or the activities previously carried out by the student and the learning objectives of the MSc in Data Science for Economics and Health).
The Admission Board has also the opportunity to ask the applicant an oral, technical interview through an online platform (e.g., Teams, Skype, Zoom, Meet). The interview aims to verify the individual knowledge and skills required by DSEH. A complete, detailed list of topics that can be asked during the interview is published on the DSEH website.
The DSEH program has also the opportunity to define a maximum number of students to be admitted, determined each year by the appropriate academic bodies on the basis of structural, instrumental, and personnel resources that can be employed to enforce the degree course.
Candidates for admission to the master's degree course may come from various bachelor's, but must have earned at least 30 ECTS in computer science and mathematics (scientific disciplinary sectors: from MAT-01 to MAT-09, INF-01, ING- INF/05) and/or in the area of economic sciences and statistics (scientific disciplinary sectors: SECS-S/01, SECS-S/02, SECS-S/03, SECS-S/06, SECS-P/05, SECS-P/01, SECS-P/02, SECS-P/03, SECS-P/07, SECS-P/08, SECS-P/10), and/or in medical sciences (scientific sector MED/01 only).
Curricular requirements must be met by the date of effective submission of the application for admission. Students with a foreign qualification are required to provide an Italian qualification to show that they satisfy the minimum curricular requirements of DSEH.
2. Proficiency in English
Proficiency in English at a B2 level or higher per the Common European Framework of Reference for Languages (CEFR) is required for admission.
The B2-level requirement will be ascertained by the University Language Centre (SLAM) upon admission as follows:
- Language certificate of B2 or higher level issued no more than three years before the date of admission application. You will find the list of language certificates recognized by the University at: https://www.unimi.it/en/node/39322. The certificate must be uploaded when submitting the online application;
- English level achieved during a University of Milan degree programme and certified by the University Language Centre (SLAM) no more than four years before the date of admission application, including levels based on language certificates submitted by the applicant during their Bachelor's degree at the University of Milan. In this case the process is automatic, the applicant does not have to attach any certificates to the application;
- Entry test administrated by the University Language Centre (SLAM) according to the calendar published on the website: (https://www.unimi.it/en/node/39267/)
All those who fail to submit a valid certificate or do not meet the required proficiency level will be instructed during the admission procedure to take the Entry test.
Applicants who do not take or pass the Entry test will be required to obtain a language proficiency certificate recognized by the University (see https://www.unimi.it/en/node/39322) and deliver it to the SLAM via the InformaStudenti service by the deadline fixed for the master's programme (https://www.unimi.it/en/node/39267/).
Applicants who do not meet the requirement by said deadline will not be admitted to the master's degree programme and may not sit any further tests.
3. Personal competencies and skills: assessment criteria
Satisfying minimum curricular requirements is a necessary but not sufficient condition for admission. An Admission Board appointed by the Faculty Board (a.k.a. Collegio Didattico) must evaluate and manage the admission procedures of candidate students.
Assessment of personal competencies and skills of applicants is enforced through a written online admission test in English. An admission threshold is set for the test by the Admission Board, and applicants must obtain a result over the threshold for passing the test. Applicants who do not participate or obtain a result over the threshold are not admitted to the master's degree programme and are not allowed to participate in any further test. Further information about the test and the related organization are published on the degree course website when the call for admissions is opened.
For applicants who meet the curricular requirements and obtain a result over the threshold in the admission test, the Admission Board assesses the personal competencies and skills of students based on the academic curriculum (quality of the previous degree as well as the average grade obtained in the bachelor program; grades obtained in mathematics, statistics, computer science and economics courses are part of the evaluation) and choice coherence (coherence between the academic curriculum and/or the activities previously carried out by the student and the learning objectives of the MSc in Data Science for Economics and Health).
The Admission Board has also the opportunity to ask the applicant an oral, technical interview through an online platform (e.g., Teams, Skype, Zoom, Meet). The interview aims to verify the individual knowledge and skills required by DSEH. A complete, detailed list of topics that can be asked during the interview is published on the DSEH website.
The DSEH program has also the opportunity to define a maximum number of students to be admitted, determined each year by the appropriate academic bodies on the basis of structural, instrumental, and personnel resources that can be employed to enforce the degree course.
Admission
Application for admission: from 22/01/2025 to 30/06/2025
Application for matriculation: from 02/04/2025 to 15/01/2026
Attachments and documents
Online services
Learn more:
Programme description and courses list
Compulsory
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Coding for Data Science and Data Management | 12 | 80 | English | First four month period | INF/01 SECS-S/01 |
| Data-Driven Economic Analysis | 12 | 80 | English | Second four month period | SECS-P/01 SECS-P/02 SECS-P/05 |
| Machine Learning and Statistical Learning | 12 | 80 | English | Second four month period | INF/01 SECS-S/01 |
| Statistical Theory and Mathematics | 12 | 80 | English | First four month period | MAT/08 SECS-S/01 |
Optional activities and study plan rules
1 - 1 activity among the selected path:
Dynamic Economic Modeling for "Data Science" and "Economic Data Analysis" paths
Introduction to Biostatistics and Epidemiology for "Health" path
Dynamic Economic Modeling for "Data Science" and "Economic Data Analysis" paths
Introduction to Biostatistics and Epidemiology for "Health" path
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Dynamic Economic Modeling | 9 | 60 | English | Third four month period | SECS-P/01 |
| Introduction to Biostatistics and Epidemiology | 9 | 60 | English | Third four month period | MED/01 |
be activated by the A.Y. 2026/2027
Compulsory
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Data Governance: Ethical and Legal Issues | 6 | 40 | English | First four month period | IUS/09 IUS/20 |
| Privacy, Data Protection and Massive Data Analysis in Emerging Scenarios | 12 | 80 | English | INF/01 | |
| Final Exam | 12 | 0 | English | Open sessions |
Optional activities and study plan rules
2 - 3 activities among the selected path
Total 18 credits/ects
Total 18 credits/ects
3 - DATA SCIENCE PATH
3 courses chosen from the following
3 courses chosen from the following
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Advanced Multivariate Statistics | 6 | 40 | English | First four month period | SECS-S/01 |
| Bayesian Analysis | 6 | 40 | English | Second four month period | SECS-S/01 |
| Chemometrics | 6 | 40 | English | Second four month period | CHIM/01 SECS-S/01 |
| Functional and Topological Data Analysis | 6 | 40 | English | Second four month period | MAT/06 |
| Marketing Analytics | 6 | 40 | English | First four month period | SECS-P/08 |
| Natural Language Processing | 6 | 40 | English | Second four month period | INF/01 |
| Network Science | 6 | 40 | English | First four month period | INF/01 |
| Organizations, Innovations, and Intelligent Technologies | 6 | 40 | English | Second four month period | SECS-P/10 |
| Probabilistic Modeling | 6 | 40 | English | Second four month period | SECS-S/01 |
| Reinforcement Learning | 6 | 40 | English | Second four month period | INF/01 |
| Scientific Data Visualization | 6 | 40 | English | First four month period | INF/01 SECS-S/01 |
| Time Series and Forecasting | 6 | 40 | English | First four month period | SECS-P/05 |
4 - ECONOMIC DATA ANALYSIS PATH
3 courses chosen from the following
At least 2 among "Advanced Causal Inference and Policy Evaluation", "Time Series and Forecasting", and "Environmental Data analysis and Policy".
3 courses chosen from the following
At least 2 among "Advanced Causal Inference and Policy Evaluation", "Time Series and Forecasting", and "Environmental Data analysis and Policy".
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Advanced Causal Inference and Policy Evaluation | 6 | 40 | English | First four month period | SECS-P/01 |
| Advanced Multivariate Statistics | 6 | 40 | English | First four month period | SECS-S/01 |
| Applied Climate Economics | 6 | 40 | English | First four month period | AGR/01 |
| Bayesian Analysis | 6 | 40 | English | Second four month period | SECS-S/01 |
| Environmental Data Analysis and Policy | 6 | 40 | English | Second four month period | SECS-P/01 |
| Global and Climate Change Economics | 6 | 40 | English | First four month period | SECS-P/01 |
| Natural Language Processing | 6 | 40 | English | Second four month period | INF/01 |
| Network Science | 6 | 40 | English | First four month period | INF/01 |
| Probabilistic Modeling | 6 | 40 | English | Second four month period | SECS-S/01 |
| Reinforcement Learning | 6 | 40 | English | Second four month period | INF/01 |
| Scientific Data Visualization | 6 | 40 | English | First four month period | INF/01 SECS-S/01 |
| Time Series and Forecasting | 6 | 40 | English | First four month period | SECS-P/05 |
5 - HEALTH PATH
3 courses chosen from the following
At least 1 among "Advanced Biostatistics and Epidemiology" and "Fundamentals of Artificial Intelligence for Data Analysis in Molecular Epidemiology".
3 courses chosen from the following
At least 1 among "Advanced Biostatistics and Epidemiology" and "Fundamentals of Artificial Intelligence for Data Analysis in Molecular Epidemiology".
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Advanced Biostatistics and Epidemiology | 6 | 40 | English | First four month period | MED/01 |
| Advanced Causal Inference and Policy Evaluation | 6 | 40 | English | First four month period | SECS-P/01 |
| Advanced Multivariate Statistics | 6 | 40 | English | First four month period | SECS-S/01 |
| Bayesian Analysis | 6 | 40 | English | Second four month period | SECS-S/01 |
| Chemometrics | 6 | 40 | English | Second four month period | CHIM/01 SECS-S/01 |
| Fundamentals of Artificial Intelligence for Data Analysis in Molecular Epidemiology | 6 | 40 | English | First four month period | MED/01 |
| Natural Language Processing | 6 | 40 | English | Second four month period | INF/01 |
| Network Science | 6 | 40 | English | First four month period | INF/01 |
| Probabilistic Modeling | 6 | 40 | English | Second four month period | SECS-S/01 |
| Reinforcement Learning | 6 | 40 | English | Second four month period | INF/01 |
| Scientific Data Visualization | 6 | 40 | English | First four month period | INF/01 SECS-S/01 |
Optional activities and study plan rules
6 - Elective activities.
Students must earn 9 credits by freely choosing from all the courses activated by the University, provided that they are culturally consistent with their educational path.
Students must earn 9 credits by freely choosing from all the courses activated by the University, provided that they are culturally consistent with their educational path.
7 - Students with Italian qualification must earn 3 credits as Transversal Skills (please check https://www.unimi.it/en/study/bachelor-and-master-study/following-your-programme-study/soft-skills). As alternative, students can select a laboratory ascribed as Transversal Skill as well (please check https://dse.cdl.unimi.it/en/courses/laboratories).
Students with a foreign qualification must earn 3 credits as Additional Language skills: Italian (please check https://dse.cdl.unimi.it/en/courses/italian-language-foreigners-tests-and-courses), instead of Transversal Skills.
Students with a foreign qualification must earn 3 credits as Additional Language skills: Italian (please check https://dse.cdl.unimi.it/en/courses/italian-language-foreigners-tests-and-courses), instead of Transversal Skills.
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Additional Language Skills: Italian (3 ECTS) | 3 | 0 | Italian | Open sessions | |
| Transversal Skills | 3 | 20 | English | Open sessions |
8 - Students must earn 3 credits by selecting one of the following alternatives:
- Internship or stage in companies, public or private bodies, professional orders;
- Training and orientation internship.
- Internship or stage in companies, public or private bodies, professional orders;
- Training and orientation internship.
| Courses or activities | ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Internship or stage in companies, public or private bodies, professional orders | 3 | 20 | English | Open sessions | |
| Training and Orientation Internship | 3 | 20 | English | Open sessions |
Learn more
Course locations
Teaching locations in the "Città Studi" area: via Celoria 18, via Celoria 20, via Golgi 19, via Venezian 15. Teaching locations in the "Political Science" area: via Conservatorio 7, via Passione 13/15
Head of study programme
Academic guidance tutor
Erasmus and international mobility tutor
Internship tutor
Seminar and workshop tutor
Reference structures
Contacts
- Disability Referee: Prof.ssa Silvia Salini
- Didactic Secretariat
Via Celoria, 18 20133 Milan
https://informastudenti.unimi.it/saw/ess?AUTH=SAML - Student Registrar
Via Santa Sofia 9
https://www.unimi.it/en/study/student-services/welcome-desk-informastudenti
+39+39 02 5032 5032
The tuition fees for students enrolled in Bachelor's, Master's and single-cycle degree programmes are divided into two instalments with different calculation methods and payment schedules:
- The amount of the first instalment is the same for all students
- The amount of the second instalment varies according to the ISEE University value, the degree programme and the student status (on track / off track for one year or off track for more than a year)
- An additional fee is due for online programmes
The University also offers:
- Concessions for students meeting high merit requirements
- Diversified tuition fees according to the student's home country for international students with assets/income abroad
- Concessions for international students with refugee status
Scholarships and benefits
The University provides a range of financial benefits to students meeting special requirements (merit, financial or personal conditions, international students).
Learn more
Guidance:
Admission, ranking and enrolment