Data Science for Economics and 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.
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.
Employment statistics (Almalaurea)
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
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
Admissions A.Y. 2026/2027
Admission applications for Academic Year 2026/2027 are now open. Non-EU students visa applicants are required to apply for admission no later than 30 April 2026.
| Courses or activities | Max 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 |
Dynamic Economic Modeling for "Data Science" and "Economic Data Analysis" paths
Introduction to Biostatistics and Epidemiology for "Health" path
| Courses or activities | Max 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 |
| Courses or activities | Max 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 | NN |
Total 18 credits/ects
3 courses chosen from the following
| Courses or activities | Max 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 | First 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 | Third 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 |
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 | Max 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 | First 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 | Third 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 |
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 | Max 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 | First 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 | Third four month period | INF/01 |
| Scientific Data Visualization | 6 | 40 | English | First four month period | INF/01 SECS-S/01 |
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 with a foreign qualification must earn 3 credits as Additional Language skills: Italian (please check https://dseh.cdl.unimi.it/en/courses/italian-language-foreigners-tests-and-courses), instead of Transversal Skills.
| Courses or activities | Max ECTS | Total hours | Language | Lesson period | SSD |
|---|---|---|---|---|---|
| Additional Language Skills: Italian (3 ECTS) | 3 | 0 | Italian | Open sessions | NN |
| Transversal Skills | 3 | 20 | English | Open sessions | NN |
- Internship or stage in companies, public or private bodies, professional orders;
- Training and orientation internship.
| 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 | NN |
| Training and Orientation Internship | 3 | 20 | English | Open sessions | NN |
- 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).
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