Data Science for Economics (Classe LM-data)-Enrolled from 2022/23 Academic Year
The graduates of the DSE 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, marketing, and business.
The DSE course enforces 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 enforces 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 DSE course allows a student to autonomously customize/specialize the study plan according to her/his own inclination by choosing elective courses for a total of 18 ECTS within two different educational paths, namely the "Data Science" path and the "Economic Data Analysis" 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 the area of policy or investment assessment, the study of production processes, and the evolution of social phenomena.
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 fulfillment of the course of study and the apprenticeship that started with the choice of the educational path.
The courses of DSE, 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 they prepare the students also for selective procedures of PhD and research programs in the areas of Data Science, Computer Science, and Economics.
The DSE course enforces 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 enforces 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 DSE course allows a student to autonomously customize/specialize the study plan according to her/his own inclination by choosing elective courses for a total of 18 ECTS within two different educational paths, namely the "Data Science" path and the "Economic Data Analysis" 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 the area of policy or investment assessment, the study of production processes, and the evolution of social phenomena.
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 fulfillment of the course of study and the apprenticeship that started with the choice of the educational path.
The courses of DSE, 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 they prepare the students also for selective procedures of PhD and research programs in the areas of Data Science, Computer Science, and Economics.
The MSc program in Data Science for Economics 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.
Employment statistics (Almalaurea)
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.
Employment statistics (Almalaurea)
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).
The ownership of linguistic skills at least at B2 level in the English language is a requirement for access.
Proficiency in English at a B2 level or higher, under 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 at or above B2, obtained no more than three years earlier. For the list of language certificates recognized by the University please review: https://www.unimi.it/en/node/39267/). The certificate must be uploaded when submitting the online application;
-English level achieved during a Bachelor's degree programme through SLAM courses and tests. The test must have been passed within the last four years. It will be assessed administratively, without the applicant having to attach any certificates;
-Placement Test delivered by the University Language Centre (SLAM), which will take place according to the schedule posted to 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 invited to take the test through the admission procedure.
Candidates who do not sit or pass the placement test will have until 30 June 2022 (Extra EU) or by 31 July 2022 (EU) to obtain and submit a recognized certificate to SLAM.
Students who do not meet the requirement by 30 June 2022 (Extra EU) or by 31 July 2022 (EU) will not be admitted to the Master's degree programme and may not sit further tests
Minimum curricular requirements cannot be considered as a verification of personal competencies and skills, which is mandatory. Admission is conditional and it depends on the assessment of the personal competencies and skills of the student provided by the Admission Board, whose members are appointed by the Faculty Board-Collegio Didattico.
Assessment of personal competencies and skills is 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 also 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).
The Admission Board reserves the possibility to request the applicant an oral interview or written text for admission, held in English language and exclusively done via electronic devices (i.e., via Teams, Skype, Zoom or other platforms). The oral interview is aimed at verifying the individual knowledge and skills required by DSE. A complete, detailed list of topics that can be asked during the interview is published on the DSE website.
Students with a foreign qualification are also required to ascertain the basic requirements equivalent to the minimum requirements for students with an Italian qualification.
Students without an Italian degree or diploma must obtain 3 credits in Additional language skills: Italian by proving an Italian language proficiency at level A2 within the Common European Framework of Reference for Languages (CEFR). This level can be assessed by the end of the degree course in the following ways:
_ by submitting the language certificate achieved no more than three years prior to the submission, at level A2 or higher, recognised by the University (the list of recognised language certificates can be found at: https://www.unimi.it/en/node/349/). The language certificate must be uploaded through the service https://informastudenti.unimi.it/saw/ess?AUTH=SAML, by choosing the category SLAM;
_ by an entry-level test, organised by SLAM, which can be taken at the beginning of every semester.
Students who fail to reach level A2 will have to attend a 60-hour Italian course organised by SLAM and to pass the final test in order to earn 3 ECTS credits of Additional Language Skills: Italian.
The DSE program also reserves the right to evaluate the possible definition of a planned maximum number of students, determined each year by the competent academic bodies, on the basis of structural, instrumental, and personnel resources available for the functioning of the degree course.
The ownership of linguistic skills at least at B2 level in the English language is a requirement for access.
Proficiency in English at a B2 level or higher, under 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 at or above B2, obtained no more than three years earlier. For the list of language certificates recognized by the University please review: https://www.unimi.it/en/node/39267/). The certificate must be uploaded when submitting the online application;
-English level achieved during a Bachelor's degree programme through SLAM courses and tests. The test must have been passed within the last four years. It will be assessed administratively, without the applicant having to attach any certificates;
-Placement Test delivered by the University Language Centre (SLAM), which will take place according to the schedule posted to 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 invited to take the test through the admission procedure.
Candidates who do not sit or pass the placement test will have until 30 June 2022 (Extra EU) or by 31 July 2022 (EU) to obtain and submit a recognized certificate to SLAM.
Students who do not meet the requirement by 30 June 2022 (Extra EU) or by 31 July 2022 (EU) will not be admitted to the Master's degree programme and may not sit further tests
Minimum curricular requirements cannot be considered as a verification of personal competencies and skills, which is mandatory. Admission is conditional and it depends on the assessment of the personal competencies and skills of the student provided by the Admission Board, whose members are appointed by the Faculty Board-Collegio Didattico.
Assessment of personal competencies and skills is 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 also 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).
The Admission Board reserves the possibility to request the applicant an oral interview or written text for admission, held in English language and exclusively done via electronic devices (i.e., via Teams, Skype, Zoom or other platforms). The oral interview is aimed at verifying the individual knowledge and skills required by DSE. A complete, detailed list of topics that can be asked during the interview is published on the DSE website.
Students with a foreign qualification are also required to ascertain the basic requirements equivalent to the minimum requirements for students with an Italian qualification.
Students without an Italian degree or diploma must obtain 3 credits in Additional language skills: Italian by proving an Italian language proficiency at level A2 within the Common European Framework of Reference for Languages (CEFR). This level can be assessed by the end of the degree course in the following ways:
_ by submitting the language certificate achieved no more than three years prior to the submission, at level A2 or higher, recognised by the University (the list of recognised language certificates can be found at: https://www.unimi.it/en/node/349/). The language certificate must be uploaded through the service https://informastudenti.unimi.it/saw/ess?AUTH=SAML, by choosing the category SLAM;
_ by an entry-level test, organised by SLAM, which can be taken at the beginning of every semester.
Students who fail to reach level A2 will have to attend a 60-hour Italian course organised by SLAM and to pass the final test in order to earn 3 ECTS credits of Additional Language Skills: Italian.
The DSE program also reserves the right to evaluate the possible definition of a planned maximum number of students, determined each year by the competent academic bodies, on the basis of structural, instrumental, and personnel resources available for the functioning of the degree course.
One of the most effective policies adopted by European Union in the last years has been the internationalization of higher education. The various Erasmus programmes that have been implemented since the nineties have greatly increased the mobility of European students.
Being a brand-new programme with an internationally oriented educational core strategy, DSE promotes a wide internationalization of their students, and therefore strongly encourages them to spend part of their studies abroad in Erasmus+ Programmes.
Erasmus+ provides opportunities to study, train, gain work experience and skills. Students can go abroad from 3 up to 12 months (including a complementary traineeship period, if planned), and may receive additional grants for studying or training. At the end of their foreign stay, students get full recognition of completed activities in terms of credits for their degree. Student mobility is carried out in the framework of prior "inter-institutional agreements" between the sending and receiving institutions.
Students can also join the traineeship programme (Placement), by going abroad from 2 up to 12 months, starting their traineeship from the first year of study. For a traineeship which is an integral part of the curriculum, the sending institution must give full academic recognition for the period spent abroad. For a traineeship that is not part of the curriculum of the student, the sending institution shall at least provide recognition by recording this period in the Diploma Supplement or, in the case of recent graduates, by providing a traineeship certificate. Traineeship may also be established with private and public companies, educational or research centers other than the hosting institution, especially in the field of finance.
Being a brand-new programme with an internationally oriented educational core strategy, DSE promotes a wide internationalization of their students, and therefore strongly encourages them to spend part of their studies abroad in Erasmus+ Programmes.
Erasmus+ provides opportunities to study, train, gain work experience and skills. Students can go abroad from 3 up to 12 months (including a complementary traineeship period, if planned), and may receive additional grants for studying or training. At the end of their foreign stay, students get full recognition of completed activities in terms of credits for their degree. Student mobility is carried out in the framework of prior "inter-institutional agreements" between the sending and receiving institutions.
Students can also join the traineeship programme (Placement), by going abroad from 2 up to 12 months, starting their traineeship from the first year of study. For a traineeship which is an integral part of the curriculum, the sending institution must give full academic recognition for the period spent abroad. For a traineeship that is not part of the curriculum of the student, the sending institution shall at least provide recognition by recording this period in the Diploma Supplement or, in the case of recent graduates, by providing a traineeship certificate. Traineeship may also be established with private and public companies, educational or research centers other than the hosting institution, especially in the field of finance.
No obligation
Courses list
First trimester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Compulsory | ||||
Coding for Data Science and Data Management | 12 | 80 | English | INF/01 SECS-S/01 |
Statistical Theory and Mathematics | 12 | 80 | English | MAT/08 SECS-S/01 |
Second trimester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Compulsory | ||||
Data-Driven Economic Analysis | 12 | 80 | English | SECS-P/01 SECS-P/02 SECS-P/05 |
Third trimester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Compulsory | ||||
Dynamic Economic Modeling | 9 | 60 | English | SECS-P/01 |
Machine Learning and Statistical Learning | 12 | 80 | English | INF/01 SECS-S/01 |
be activated by the A.Y. 2023/2024
Open sessions
There are no specific sessions for these activities (e.g. open online courses).
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Compulsory | ||||
Cybersecurity and Protection of Personal Data: Legal and Policies Issues | 6 | 40 | English | IUS/09 IUS/20 |
Privacy, Data Protection and Massive Data Analysis in Emerging Scenarios | 12 | 80 | English | INF/01 |
Optional | ||||
Advanced Multivariate Statistics | 6 | 40 | English | SECS-S/01 |
Bayesian Analysis | 6 | 40 | English | SECS-S/01 |
Causal Inference and Policy Evaluation** | 6 | 40 | English | SECS-P/01 |
Experimental Methods and Behavioural Economics** | 6 | 40 | English | SECS-P/01 |
Functional and Topological Data Analysis | 6 | 40 | English | MAT/06 |
Marketing Analytics* | 6 | 40 | English | SECS-P/08 |
Network Science | 6 | 40 | English | INF/01 |
Project Management and Innovation* | 6 | 40 | English | SECS-P/10 |
Reinforcement Learning | 6 | 40 | English | INF/01 |
Text Mining and Sentiment Analysis | 6 | 40 | English | INF/01 |
Time Series and Forecasting** | 6 | 40 | English | SECS-P/05 |
Conclusive activities
There are no specific sessions for these activities (e.g. open online courses).
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Compulsory | ||||
Final Exam | 12 | 80 | English |
Optional activities and study plan rules
1 - 3 activities among the selected path
Total 18 credits/ects
Total 18 credits/ects
2 - DATA SCIENCE PATH
(3 courses chosen from the following, no more than 1 among those indicated with the symbol *)
(3 courses chosen from the following, no more than 1 among those indicated with the symbol *)
Courses or activities | Max ECTS | Total hours | Language | Lesson period | SSD |
---|---|---|---|---|---|
Advanced Multivariate Statistics | 6 | 40 | English | Open sessions | SECS-S/01 |
Bayesian Analysis | 6 | 40 | English | Open sessions | SECS-S/01 |
Functional and Topological Data Analysis | 6 | 40 | English | Open sessions | MAT/06 |
Marketing Analytics* | 6 | 40 | English | Open sessions | SECS-P/08 |
Network Science | 6 | 40 | English | Open sessions | INF/01 |
Project Management and Innovation* | 6 | 40 | English | Open sessions | SECS-P/10 |
Reinforcement Learning | 6 | 40 | English | Open sessions | INF/01 |
Text Mining and Sentiment Analysis | 6 | 40 | English | Open sessions | INF/01 |
Time Series and Forecasting** | 6 | 40 | English | Open sessions | SECS-P/05 |
3 - ECONOMIC DATA ANALYSIS PATH
(3 courses chosen from the following, at least 2 of those indicated with the symbol **)
(3 courses chosen from the following, at least 2 of those indicated with the symbol **)
Courses or activities | Max ECTS | Total hours | Language | Lesson period | SSD |
---|---|---|---|---|---|
Advanced Multivariate Statistics | 6 | 40 | English | Open sessions | SECS-S/01 |
Bayesian Analysis | 6 | 40 | English | Open sessions | SECS-S/01 |
Causal Inference and Policy Evaluation** | 6 | 40 | English | Open sessions | SECS-P/01 |
Experimental Methods and Behavioural Economics** | 6 | 40 | English | Open sessions | SECS-P/01 |
Text Mining and Sentiment Analysis | 6 | 40 | English | Open sessions | INF/01 |
Time Series and Forecasting** | 6 | 40 | English | Open sessions | SECS-P/05 |
First semester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Optional | ||||
The Instruments of Gender Equality | 3 | 24 | Italian | IUS/08 |
Second semester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Optional | ||||
Equal Opportunities and Scientific Careers | 3 | 24 | Italian | SPS/09 |
Gender Based Violence: an Interdisciplinary Approach | 3 | 20 | Italian | IUS/08 |
Laboratory: Sustainability and Sustainable Development | 3 | 24 | Italian | AGR/01 AGR/13 IUS/01 SECS-P/01 SPS/04 |
Teaching Workshop: the Ancient Hospital of Milan. How to Lead a Guided Tour of an Historical Monument | 3 | 20 | Italian |
First trimester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Optional | ||||
Laboratory: "nutritional Epidemiology: Methods and Practice" | 3 | 20 | English | MED/01 |
Laboratory: Organized Crime and Research Methodology. | 3 | 20 | Italian | SPS/09 |
Second trimester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Optional | ||||
Laboratory "cloud and Distributed Environments for Analytics in a Luxury Brand" | 3 | 20 | English | INF/01 SECS-S/01 |
Laboratory "data Scientist for Business Communication" | 3 | 20 | Italian | INF/01 SECS-S/01 |
Laboratory "official Statistics: Organization and Data of Italian National Institute of Statistics" | 3 | 20 | Italian | SECS-S/01 |
Laboratory "reinforcement Learning" | 3 | 20 | English | INF/01 |
Laboratory "retrieving Skills for Stem Job Description and Matching with Cvs" | 3 | 20 | English | INF/01 SECS-S/01 |
Laboratory: Art, Culture and Organized Crime | 3 | 20 | Italian | SPS/07 |
Third trimester
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Optional | ||||
Laboratory "data Analytics and Digital Transformation" | 3 | 20 | English | INF/01 SECS-S/01 |
Laboratory "data Solutions for Marketing" | 3 | 20 | English | INF/01 SECS-S/01 |
Laboratory "data Valorization for Fintech" | 3 | 20 | English | INF/01 SECS-S/01 |
Laboratory "hackathon: Deploy Machine Learning Models On Google Cloud Platform" | 3 | 20 | English | INF/01 SECS-S/01 |
Laboratory "new Public Governance and Co-Production of Public Services" | 3 | 20 | Italian | SECS-P/10 |
Laboratory "personalized Health Care" | 3 | 20 | English | MED/01 |
Laboratory "text Data for Trading" | 3 | 20 | English | INF/01 SECS-S/01 |
Laboratory:societies, Rights and Environmental Crime | 3 | 20 | Italian | SPS/07 |
Laboratory:strategies for Managing Environmental Conflicts Through Participatory Mechanisms | 3 | 20 | Italian | IUS/10 |
Workshop: Anti-Mafia Journalism | 3 | 20 | Italian | SPS/08 |
Open sessions
There are no specific sessions for these activities (e.g. open online courses).
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Optional | ||||
Additional Language Skills: Italian (3 ECTS) | 3 | 0 | Italian | |
Constitution Education | 3 | 24 | Italian | IUS/08 |
Transversal Skills | 3 | 20 | English |
Conclusive activities
There are no specific sessions for these activities (e.g. open online courses).
Courses or activities | Max ECTS | Total hours | Language | SSD |
---|---|---|---|---|
Optional | ||||
Internship Or Stage in Companies, Public Or Private Bodies, Professional Orders | 3 | 20 | English | |
Training and Orientation Internships | 3 | 20 | English |
Optional activities and study plan rules
4 - Students must earn 9 credits for elective activities.
5 - Students must earn 3 credits by selecting one of the following alternatives: Foreign language: advanced; Transversal Skills, Laboratory
Courses or activities | Max 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 |
6 - Students must earn 3 credits by selecting one of the following alternatives: Internship or Stage
Courses or activities | Max 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 Internships | 3 | 20 | English | Open sessions |
Presidente del Collegio Didattico
prof.ssa Silvia Salini Vice-Presidente Collegio Didattico Prof. Stefano Montanelli
Reference structures
Contacts
- Welcome desk
Via Santa Sofia 9
https://www.unimi.it/en/study/student-services/welcome-desk-informastudenti
+39+39 02 5032 5032 - Web Site
https://mls.cdl.unimi.it/en - Twitter
https://twitter.com/DseUnimi - Facebook
http://www.facebook.com/dseunimi - Instagram
https://www.instagram.com/dse_unimi/ - Linkedin
https://www.linkedin.com/company/74033091/admin/
A.Y. 2025/2026
A.Y. 2024/2025
A.Y. 2023/2024
A.Y. 2022/2023
Official documents