Data science for economics (DSE)
The use of the term “data science” is increasingly common, as is “big data.” But what does it mean? Is there something unique about it? What skills do “data scientists” need to be productive in a world deluged by data? What are the implications for scientific inquiry?
Vasant Dhar. 2013. Data science and prediction. Commun. ACM 56, 12 (December 2013), 64–73.
We have run out of adjectives and superlatives to describe the growth trends of data. The technology revolution has brought about the need to process, store, analyze, and comprehend large volumes of diverse data in meaningful ways. However, the value of the stored data is zero unless it is acted upon. The scale of data volume and variety places new demands on organizations to quickly uncover hidden relationships and patterns. This is where data science techniques have proven to be extremely useful. They are increasingly finding their way into the everyday activities of many business and government functions, whether in identifying which customers are likely to take their business elsewhere, or mapping flu pandemic using social media signals.
Vijay Kotu, Bala Deshpande. 2019. Data Science Concepts and Practice. Morgan Kaufmann.
The Master of Science in “Data Science for Economics” (DSE) aims to provide a modern, effective educational programme for students interested in data science issues, with special focus on applications to the economic field.
The DSE programme started in 2018 and it has been re-designed in 2022 to join the emerging “LM-DATA” CUN class.
DSE strongly leverages STEM disciplines to provide a solid, coherent training on quantitative and methodological methods and tools of Information Technology (IT) as well as Statistics and Mathematics to interpret and analyze complex phenomena in the field of economy. DSE is conceived as a flexible educational programme with an important number of elective courses. Supported by the tutors, a student customizes the study plan through the choice between two alternative paths, namely “Data Science” and “Economic Data Analysis” paths, to further enhance STEM-oriented and economic-oriented competences, respectively. The external stakeholders of DSE are constituted by selected territorial companies and organizations focused on data science missions, and they are widely involved in the programme development in the form of lab and internship opportunities.
Given the multidisciplinary nature of the acquired knowledge and skills, the graduates of DSE can work in a variety of professional areas: small, medium, and large IT companies and research centers, companies and public bodies focused on big data management, R&D labs, innovative start-ups, healthcare companies, biomedical and pharmaceutical industries, economic and financial consulting firms, Public Administrations, National Statistical Institutes, National Banks.
Given their solid methodological education, the graduates of DSE can continue their academic experience in a PhD programme; possible scientific fields are Computer Science, Mathematics, Statistics, and Economics.
The DSE 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 DSE 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 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 fulfilment of the course of study and the learning process began 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 also pave the way to students interested in PhD and research programs in the areas of Data Science, Computer Science, and Economics.
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.
Statistiche occupazionali (Almalaurea)
Students can apply to the double-degree program with the "Data Science and Business Analytics - (120 ECTS)", a master-program jointly organized by the Faculty of Economic Sciences, University of Warsaw. Detailed information on the agreement, how to apply, and joint curricula can be found at https://dse.cdl.unimi.it/en
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).
Curricular requirements must be met by the date of effective submission of the application for admission.
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;
- Placement 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 placement test.
Applicants who do not take or pass the placement 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
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 will be ascertained through a written online admission test, held in English language. A more detailed description of the test content and how the test will be structured and organised will be made available on the degree course website close to the opening of admissions. Candidates who do not sit or reach the minimum level required from the Admission Board in the admission test will not be admitted to the master's degree programme and cannot sit further tests.
For candidates who both meet the curricular requirements and reach the minimum level in the admission test, 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 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 also reserves the possibility to request the applicant an oral interview (i.e., via Teams, Skype, Zoom or other platforms). The oral interview aims to verify 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.
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.
To obtain the degree, those who do not hold an Italian high school diploma or bachelor's degree must demonstrate proficiency in Italian at the A2 or higher level per the Common European Framework of Reference for Languages (CEFR). This level must be demonstrated prior to completing the course programme in one of the following ways:
- by submitting a certificate of A2 or higher level issued no more than three years prior to the date of submission. You will find the list of language certificates recognized by the University at: https://www.unimi.it/en/node/349/). The language certificate must be submitted to the University Language Centre (SLAM) via the Language Test category of the InformaStudenti service: https://informastudenti.unimi.it/saw/ess?AUTH=SAML;
v- ia an entry-level test administrated by SLAM that can be taken only once and is compulsory for all students who do not have a valid language certificate. Those who fail to reach A2 level will have to attend one or more than one 60-hour Italian course(s) geared to their level.
Those who do not take the entry-level test or fail to pass the end-of-course test after six attempts will have to obtain language certification privately in order to earn the 3 credits of Additional language skills: Italian.
Ammissione
Domanda di ammissione: dal 22/01/2024 al 30/06/2024
Domanda di immatricolazione: dal 08/04/2024 al 15/01/2025
Allegati e documenti
Note
Gli studenti non UE richiedenti visto sono tenuti a presentare domanda di ammissione entro e non oltre il 30 aprile 2024. Le domande presentate oltre i termini non saranno valutate e non sarà in nessun caso possibile richiedere il rimborso del contributo di ammissione.
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Obbligatorio | ||||
Machine learning and statistical learning | 12 | 80 | Inglese | INF/01 SECS-S/01 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Obbligatorio | ||||
Coding for data science and data management | 12 | 80 | Inglese | INF/01 SECS-S/01 |
Statistical theory and mathematics | 12 | 80 | Inglese | MAT/08 SECS-S/01 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Obbligatorio | ||||
Data-driven economic analysis | 12 | 80 | Inglese | SECS-P/01 SECS-P/02 SECS-P/05 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Obbligatorio | ||||
Dynamic economic modeling | 9 | 60 | Inglese | SECS-P/01 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Obbligatorio | ||||
Cybersecurity and protection of personal data: legal and policies issues | 6 | 40 | Inglese | IUS/09 IUS/20 |
Privacy, data protection and massive data analysis in emerging scenarios | 12 | 80 | Inglese | INF/01 |
Facoltativo | ||||
Advanced multivariate statistics | 6 | 40 | Inglese | SECS-S/01 |
Causal inference and policy evaluation** | 6 | 40 | Inglese | SECS-P/01 |
Marketing analytics* | 6 | 40 | Inglese | SECS-P/08 |
Network science | 6 | 40 | Inglese | INF/01 |
Time series and forecasting** | 6 | 40 | Inglese | SECS-P/05 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Facoltativo | ||||
Bayesian analysis | 6 | 40 | Inglese | SECS-S/01 |
Experimental methods and behavioural economics** | 6 | 40 | Inglese | SECS-P/01 |
Functional and topological data analysis | 6 | 40 | Inglese | MAT/06 |
Project management and innovation* | 6 | 40 | Inglese | SECS-P/10 |
Reinforcement learning | 6 | 40 | Inglese | INF/01 |
Text mining and sentiment analysis | 6 | 40 | Inglese | INF/01 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Obbligatorio | ||||
Final exam | 12 | 80 | Inglese |
Total 18 credits/ects
(3 courses chosen from the following, no more than 1 among those indicated with the symbol *)
Attività formative | Crediti | Ore totali | Lingua | Periodo | SSD |
---|---|---|---|---|---|
Advanced multivariate statistics | 6 | 40 | Inglese | Primo trimestre | SECS-S/01 |
Bayesian analysis | 6 | 40 | Inglese | Secondo trimestre | SECS-S/01 |
Functional and topological data analysis | 6 | 40 | Inglese | Secondo trimestre | MAT/06 |
Marketing analytics* | 6 | 40 | Inglese | Primo trimestre | SECS-P/08 |
Network science | 6 | 40 | Inglese | Primo trimestre | INF/01 |
Project management and innovation* | 6 | 40 | Inglese | Secondo trimestre | SECS-P/10 |
Reinforcement learning | 6 | 40 | Inglese | Secondo trimestre | INF/01 |
Text mining and sentiment analysis | 6 | 40 | Inglese | Secondo trimestre | INF/01 |
Time series and forecasting** | 6 | 40 | Inglese | Primo trimestre | SECS-P/05 |
(3 courses chosen from the following, at least 2 of those indicated with the symbol **)
Attività formative | Crediti | Ore totali | Lingua | Periodo | SSD |
---|---|---|---|---|---|
Advanced multivariate statistics | 6 | 40 | Inglese | Primo trimestre | SECS-S/01 |
Bayesian analysis | 6 | 40 | Inglese | Secondo trimestre | SECS-S/01 |
Causal inference and policy evaluation** | 6 | 40 | Inglese | Primo trimestre | SECS-P/01 |
Experimental methods and behavioural economics** | 6 | 40 | Inglese | Secondo trimestre | SECS-P/01 |
Text mining and sentiment analysis | 6 | 40 | Inglese | Secondo trimestre | INF/01 |
Time series and forecasting** | 6 | 40 | Inglese | Primo trimestre | SECS-P/05 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Facoltativo | ||||
Laboratory: "data visualization narratives" | 3 | 20 | Inglese | SECS-S/01 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Facoltativo | ||||
Laboratory "data scientist for business communication" | 3 | 20 | Italiano | INF/01 SECS-S/01 |
Laboratory "official statistics: organization and data of italian national institute of statistics" | 3 | 20 | Italiano | SECS-S/01 |
Laboratory: "data storytelling: effective visualisation of data with different tools" | 3 | 20 | Inglese | INF/01 SECS-S/01 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Facoltativo | ||||
Laboratory "hackathon: deploy machine learning models on google cloud platform" | 3 | 20 | Inglese | INF/01 SECS-S/01 |
Laboratory "personalized health care" | 3 | 20 | Inglese | MED/01 |
Laboratory: "drive digital transformation with data analytics" | 0 | 20 | Inglese | INF/01 SECS-S/01 |
Laboratory: "nutritional epidemiology: methods and practice" | 3 | 20 | Inglese | MED/01 |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Facoltativo | ||||
Additional language skills: italian (3 ECTS) | 3 | 0 | Italiano | |
Transversal skills | 3 | 20 | Inglese |
Attività formative | Crediti | Ore totali | Lingua | SSD |
---|---|---|---|---|
Facoltativo | ||||
Internship or stage in companies, public or private bodies, professional orders | 3 | 20 | Inglese | |
Training and orientation internships | 3 | 20 | Inglese |
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.
Attività formative | Crediti | Ore totali | Lingua | Periodo | SSD |
---|---|---|---|---|---|
Additional language skills: italian (3 ECTS) | 3 | 0 | Italiano | Periodo non definito |
- Internship or stage in companies, public or private bodies, professional orders;
- Training and orientation internship.
- Disability Referee
Prof.ssa Silvia Salini - Degree Course website
https://dse.cdl.unimi.it/en - Student Registrar
Via Santa Sofia 9
https://www.unimi.it/en/study/student-services/welcome-desk-informastudenti
+39+39 02 5032 5032 - Didactic Secretariat
18, Via G. Celoria, Milan
https://informastudenti.unimi.it/saw/ess?AUTH=SAML
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)
Maggiori informazioni
Orientamento:
Info su ammissioni e immatricolazioni
- Contatta le segreterie
- Sportello online InformaStudenti
- Studenti internazionali: welcome desk
- Studenti con disabilità
- Studenti con DSA