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.
Statistiche occupazionali (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
Strongly recommended.
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.
Ammissione
Domanda di ammissione: dal 22/01/2025 al 30/06/2025
Domanda di immatricolazione: dal 02/04/2025 al 15/01/2026
Ammissioni A.A. 2026/2027
Sono aperte le domande di ammissione per l'Anno Accademico 2026/2027. Gli studenti non UE richiedenti visto possono presentare domanda di ammissione entro il 30 aprile 2026.
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Coding for data science and data management | 12 | 80 | Inglese | Primo quadrimestre | INF/01 SECS-S/01 |
| Data-driven economic analysis | 12 | 80 | Inglese | Secondo quadrimestre | SECS-P/01 SECS-P/02 SECS-P/05 |
| Machine learning and statistical learning | 12 | 80 | Inglese | Secondo quadrimestre | INF/01 SECS-S/01 |
| Statistical theory and mathematics | 12 | 80 | Inglese | Primo quadrimestre | MAT/08 SECS-S/01 |
Dynamic Economic Modeling for "Data Science" and "Economic Data Analysis" paths
Introduction to Biostatistics and Epidemiology for "Health" path
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Dynamic economic modeling | 9 | 60 | Inglese | Terzo quadrimestre | SECS-P/01 |
| Introduction to biostatistics and epidemiology | 9 | 60 | Inglese | Terzo quadrimestre | MED/01 |
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Data governance: ethical and legal issues | 6 | 40 | Inglese | Primo quadrimestre | IUS/09 IUS/20 |
| Privacy, data protection and massive data analysis in emerging scenarios | 12 | 80 | Inglese | INF/01 | |
| Final exam | 12 | 0 | Inglese | Periodo non definito | NN |
Total 18 credits/ects
3 courses chosen from the following
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Advanced multivariate statistics | 6 | 40 | Inglese | Primo quadrimestre | SECS-S/01 |
| Bayesian analysis | 6 | 40 | Inglese | Secondo quadrimestre | SECS-S/01 |
| Chemometrics | 6 | 40 | Inglese | Secondo quadrimestre | CHIM/01 SECS-S/01 |
| Functional and topological data analysis | 6 | 40 | Inglese | Secondo quadrimestre | MAT/06 |
| Marketing analytics | 6 | 40 | Inglese | Primo quadrimestre | SECS-P/08 |
| Natural language processing | 6 | 40 | Inglese | Primo quadrimestre | INF/01 |
| Network science | 6 | 40 | Inglese | Primo quadrimestre | INF/01 |
| Organizations, innovations, and intelligent technologies | 6 | 40 | Inglese | Secondo quadrimestre | SECS-P/10 |
| Probabilistic modeling | 6 | 40 | Inglese | Secondo quadrimestre | SECS-S/01 |
| Reinforcement learning | 6 | 40 | Inglese | Terzo quadrimestre | INF/01 |
| Scientific data visualization | 6 | 40 | Inglese | Primo quadrimestre | INF/01 SECS-S/01 |
| Time series and forecasting | 6 | 40 | Inglese | Primo quadrimestre | 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".
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Advanced causal inference and policy evaluation | 6 | 40 | Inglese | Primo quadrimestre | SECS-P/01 |
| Advanced multivariate statistics | 6 | 40 | Inglese | Primo quadrimestre | SECS-S/01 |
| Applied climate economics | 6 | 40 | Inglese | Primo quadrimestre | AGR/01 |
| Bayesian analysis | 6 | 40 | Inglese | Secondo quadrimestre | SECS-S/01 |
| Environmental data analysis and policy | 6 | 40 | Inglese | Secondo quadrimestre | SECS-P/01 |
| Global and climate change economics | 6 | 40 | Inglese | Primo quadrimestre | SECS-P/01 |
| Natural language processing | 6 | 40 | Inglese | Primo quadrimestre | INF/01 |
| Network science | 6 | 40 | Inglese | Primo quadrimestre | INF/01 |
| Probabilistic modeling | 6 | 40 | Inglese | Secondo quadrimestre | SECS-S/01 |
| Reinforcement learning | 6 | 40 | Inglese | Terzo quadrimestre | INF/01 |
| Scientific data visualization | 6 | 40 | Inglese | Primo quadrimestre | INF/01 SECS-S/01 |
| Time series and forecasting | 6 | 40 | Inglese | Primo quadrimestre | 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".
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Advanced biostatistics and epidemiology | 6 | 40 | Inglese | Primo quadrimestre | MED/01 |
| Advanced causal inference and policy evaluation | 6 | 40 | Inglese | Primo quadrimestre | SECS-P/01 |
| Advanced multivariate statistics | 6 | 40 | Inglese | Primo quadrimestre | SECS-S/01 |
| Bayesian analysis | 6 | 40 | Inglese | Secondo quadrimestre | SECS-S/01 |
| Chemometrics | 6 | 40 | Inglese | Secondo quadrimestre | CHIM/01 SECS-S/01 |
| Fundamentals of artificial intelligence for data analysis in molecular epidemiology | 6 | 40 | Inglese | Primo quadrimestre | MED/01 |
| Natural language processing | 6 | 40 | Inglese | Primo quadrimestre | INF/01 |
| Network science | 6 | 40 | Inglese | Primo quadrimestre | INF/01 |
| Probabilistic modeling | 6 | 40 | Inglese | Secondo quadrimestre | SECS-S/01 |
| Reinforcement learning | 6 | 40 | Inglese | Terzo quadrimestre | INF/01 |
| Scientific data visualization | 6 | 40 | Inglese | Primo quadrimestre | 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.
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Additional language skills: italian (3 ECTS) | 3 | 0 | Italiano | Periodo non definito | NN |
| Transversal skills | 3 | 20 | Inglese | Periodo non definito | NN |
- Internship or stage in companies, public or private bodies, professional orders;
- Training and orientation internship.
| Attività formative | Crediti massimi | Ore totali | Lingua | Periodo | SSD |
|---|---|---|---|---|---|
| Internship or stage in companies, public or private bodies, professional orders | 3 | 20 | Inglese | Periodo non definito | NN |
| Training and orientation internship | 3 | 20 | Inglese | Periodo non definito | NN |
- Disability Referee: Prof.ssa Silvia Salini
- Didactic Secretariat
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Le tasse universitarie per gli studenti iscritti ai corsi di laurea, di laurea magistrale e a ciclo unico sono suddivise in due rate con modalità di calcolo e tempi di pagamento diversi:
- l'importo della prima rata è uguale per tutti
- l'importo della seconda rata varia in base al valore ISEE Università, al Corso di laurea di iscrizione e alla posizione (in corso/fuori corso da un anno oppure fuori corso da più di un anno).
- per i corsi on line è prevista una rata suppletiva.
Sono previste:
- agevolazioni per gli studenti con elevati requisiti di merito
- importi diversificati in base al Paese di provenienza per gli studenti internazionali con reddito e patrimonio all'estero
- agevolazioni per gli studenti internazionali con status di rifugiato
Altre agevolazioni
L’Ateneo fornisce agevolazioni economiche a favore dei propri studenti con requisiti particolari (merito, condizioni economiche o personali, studenti internazionali)
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