Data science for economics (DSE)

data
Data science for economics (DSE)
Scheda del corso
A.A. 2022/2023
Laurea magistrale
LM Data - Data science
Laurea magistrale
120
Crediti
Accesso
Libero con valutazione dei requisiti di accesso
2
Anni
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 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 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. 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
Immatricolazione
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.

Ammissione

Domanda di ammissione: dal 15/03/2022 al 30/06/2022

Domanda di immatricolazione: dal 01/04/2022 al 16/01/2023


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 31 maggio 2022. 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
sarà attivato dall'A.A. 2023/2024
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
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
Bayesian analysis 6 40 Inglese SECS-S/01
Causal inference and policy evaluation** 6 40 Inglese SECS-P/01
Experimental methods and behavioural economics** 6 40 Inglese SECS-P/01
Functional and topological data analysis 6 40 Inglese MAT/06
Marketing analytics* 6 40 Inglese SECS-P/08
Network science 6 40 Inglese INF/01
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
Time series and forecasting** 6 40 Inglese SECS-P/05
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 Periodo non definito SECS-S/01
Bayesian analysis 6 40 Inglese Periodo non definito SECS-S/01
Functional and topological data analysis 6 40 Inglese Periodo non definito MAT/06
Marketing analytics* 6 40 Inglese Periodo non definito SECS-P/08
Network science 6 40 Inglese Periodo non definito INF/01
Project management and innovation* 6 40 Inglese Periodo non definito SECS-P/10
Reinforcement learning 6 40 Inglese Periodo non definito INF/01
Text mining and sentiment analysis 6 40 Inglese Periodo non definito INF/01
Time series and forecasting** 6 40 Inglese Periodo non definito 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 Periodo non definito SECS-S/01
Bayesian analysis 6 40 Inglese Periodo non definito SECS-S/01
Causal inference and policy evaluation** 6 40 Inglese Periodo non definito SECS-P/01
Experimental methods and behavioural economics** 6 40 Inglese Periodo non definito SECS-P/01
Text mining and sentiment analysis 6 40 Inglese Periodo non definito INF/01
Time series and forecasting** 6 40 Inglese Periodo non definito SECS-P/05
Primo semestre
Attività formative Crediti Ore totali Lingua SSD
Facoltativo
Gli strumenti della parità di genere 3 24 Italiano IUS/08
Secondo semestre
Attività formative Crediti Ore totali Lingua SSD
Facoltativo
Educazione alla costituzione 3 24 Italiano IUS/08
Laboratorio: la ca' granda dei milanesi. formazione all'itinerario di visita 3 20 Italiano
Laboratorio: sostenibilità e sviluppo sostenibile (3 cfu) corso base 3 24 Italiano AGR/01 AGR/13 IUS/01 SECS-P/01 SPS/04
Pari opportunità e carriere scientifiche (G) 3 24 Italiano SPS/09
Violenza di genere: percorsi formativi interdisciplinari 3 20 Italiano IUS/08
Primo trimestre
Attività formative Crediti Ore totali Lingua SSD
Facoltativo
Laboratorio: criminalità organizzata e metodologia della ricerca 3 20 Italiano SPS/09
Laboratory: "nutritional epidemiology: methods and practice" 3 20 Inglese MED/01
Secondo trimestre
Attività formative Crediti Ore totali Lingua SSD
Facoltativo
Laboratorio: arte, cultura e criminalità organizzata 3 20 Italiano SPS/07
Laboratory "cloud and distributed environments for analytics in a luxury brand" 3 20 Inglese INF/01 SECS-S/01
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 "reinforcement learning" 3 20 Inglese INF/01
Laboratory "retrieving skills for stem job description and matching with cvs" 3 20 Inglese INF/01 SECS-S/01
Terzo trimestre
Attività formative Crediti Ore totali Lingua SSD
Facoltativo
Laboratorio "new public governance e coproduzione dei servizi pubblici" 3 20 Italiano SECS-P/10
Laboratorio: giornalismo antimafioso 3 20 Italiano SPS/08
Laboratorio: prevenzione e gestione dei conflitti ambientali: strategie e strumenti collaborativi 3 20 Italiano IUS/10
Laboratorio: società, diritti e criminalità ambientale 3 20 Italiano SPS/07
Laboratory "data analytics and digital transformation" 3 20 Inglese INF/01 SECS-S/01
Laboratory "data solutions for marketing" 3 20 Inglese INF/01 SECS-S/01
Laboratory "data valorization for fintech" 3 20 Inglese INF/01 SECS-S/01
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 "text data for trading" 3 20 Inglese INF/01 SECS-S/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.
5 - Students must earn 3 credits by selecting one of the following alternatives: Foreign language: advanced; Transversal Skills, Laboratory
Attività formative Crediti Ore totali Lingua Periodo SSD
Additional language skills: italian (3 ECTS) 3 0 Italiano Periodo non definito
Transversal skills 3 20 Inglese Periodo non definito
6 - Students must earn 3 credits by selecting one of the following alternatives: Internship or Stage
Attività formative Crediti Ore totali Lingua Periodo SSD
Internship or stage in companies, public or private bodies, professional orders 3 20 Inglese Periodo non definito
Training and orientation internships 3 20 Inglese Periodo non definito
Altre informazioni
Sedi didattiche
Department od Economics, Management and Quantitative Methods, Via Conservatorio 7, Milano Department of Computer Science "Giovanni degli Antoni", Via Celoria 18, Milano

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