Data science for economics (DSE)

data
Data science for economics (DSE)
Scheda del corso
A.A. 2024/2025
Laurea magistrale
LM Data - Data science
Laurea magistrale
120
Crediti
Accesso
Libero con valutazione dei requisiti di accesso
2
Anni
Sede
Milano
Lingua
Inglese

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 Master's degree course in Data Science for Economics (DSE), 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. 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.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 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.
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.

Statistiche occupazionali (Almalaurea)
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 in different locations such as Germany, Denmark, French and many others. 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.

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
No obligation, strongly recommended.
Immatricolazione
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).
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

Avviso di ammissione


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.

Per approfondire:
Manifesto ed elenco insegnamenti
Secondo semestre
Attività formative Crediti Ore totali Lingua SSD
Obbligatorio
Machine learning and statistical learning 12 80 Inglese INF/01 SECS-S/01
Primo trimestre
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
Secondo trimestre
Attività formative Crediti Ore totali Lingua SSD
Obbligatorio
Data-driven economic analysis 12 80 Inglese SECS-P/01 SECS-P/02 SECS-P/05
Terzo trimestre
Attività formative Crediti Ore totali Lingua SSD
Obbligatorio
Dynamic economic modeling 9 60 Inglese SECS-P/01
Primo trimestre
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
Secondo trimestre
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 conclusive
Per queste attività non è previsto un periodo di offerta (es. corsi online a frequenza libera).
Attività formative Crediti Ore totali Lingua SSD
Obbligatorio
Final exam 12 80 Inglese
Attività a scelta e regole di composizione del piano didattico
1 - 3 activities among the selected path
Total 18 credits/ects
2 - DATA SCIENCE PATH
(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 - ECONOMIC DATA ANALYSIS PATH
(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
Primo trimestre
Attività formative Crediti Ore totali Lingua SSD
Facoltativo
Laboratory: "data visualization narratives" 3 20 Inglese SECS-S/01
Secondo trimestre
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
Terzo trimestre
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
Periodo non definito
Per queste attività non è previsto un periodo di offerta (es. corsi online a frequenza libera).
Attività formative Crediti Ore totali Lingua SSD
Facoltativo
Additional language skills: italian (3 ECTS) 3 0 Italiano
Transversal skills 3 20 Inglese
Attività conclusive
Per queste attività non è previsto un periodo di offerta (es. corsi online a frequenza libera).
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
Attività a scelta e regole di composizione del piano didattico
4 - Students must earn 9 credits for elective activities (courses/laboratories).
5 - 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.
Attività formative Crediti Ore totali Lingua Periodo SSD
Additional language skills: italian (3 ECTS) 3 0 Italiano Periodo non definito
6 - 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.
Altre informazioni
Sede
Milano
Presidente del Collegio didattico
Tutor per l'orientamento
Tutor per la mobilità internazionale e l'Erasmus
Tutor per stage e tirocini
Referente AQ del corso di studio
Contatti

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