Course Objectives and Structure

The Master of Science in “Data Science and Economics” (DSE) responds to the training needs of data scientists in the economic field. The course provides skills to analyze and understand the nature of data through modern data management techniques, machine learning, data mining and cloud computing. Students will learn to extract meaningful relationships and recurring patterns, build predictive and nowcasting models that integrate company, market, administrative and social media data, perform analysis of policy effects (economic, social) or actions (investments, marketing campaigns) and any other activity related to the sectors of economy, marketing, business and finance.

The degree program aims to provide a solid and modern cultural background in computer science, statistics, and economics, providing an integrated view of these skills in all its courses, in the belief that the integration of the foundational disciplines can develop for students a strong added value compared to the mere sum of skills acquired separately. The innovation in the teaching methods also has the ambition to develop, in students, the specific methodological attitude of the data scientist, forming professional figures capable of thinking in a new way the reality, starting from the challenges, thinking in terms of models, understanding the value of data, and learning how to evaluate the real impact of choices.

To this end, the modality of frontal transmission of skills will be integrated with laboratory activities that develop the ability to work in groups starting from real problems and using real data. Methods of work such as hackathons, problem-solving, challenges among working groups, which already constitute personnel selection tools at the most important companies operating in the data sector, will be used intensively in the degree course with the training objective to develop the methodological attitude expected for the data scientist. The case studies and laboratory simulations will replace, as often as possible, the use of real data, without renouncing the complexity; these case studies will involve companies, research centers, institutions, economic and financial operators, communication agencies and marketing in the design of activities and interaction with students.

Entry Requirements

The successful candidate of the Master program in Data Science and Economics should have adequate knowledge of computer science, mathematics, economics, and statistics at an undergraduate level.

Applicants have to submit their curriculum vitae, the transcript of exams and academic career, a motivation letter. Candidates can optionally ask their advisors to provide a presentation letter.

Applicants satisfying the entry requirements would be asked for a telematics interview aimed at substantive verification of their background, their motivations and their fluency in English.

The minimal curricular requirements are:

  • 12 ECTS credits in computer science and mathematics
  • 12 ECTS credits in economics and
  • English language knowledge, level B2 or higher

Career Prospects

The MSc program in Data Science and Economics aims to train the following professional figures.

Profile: Data Scientist

Functions: Analyze and elaborate forecasts on large data flow, identifying and applying the most appropriate software tools and statistical techniques for their elaboration, and create sophisticated models for predictive data-driven analysis.

Skills: Statistical analysis, programming and knowledge of software tools.

Outlets: small and medium-sized enterprises, start-ups and public administration.

Profile: Data-Driven Economist

Functions: Frame problems of economic analysis in the context of data science by identifying data and technologies that can provide new keys for reading or evaluating economic and social phenomena.

Skills: Economic theory, statistical and computer techniques

Outlets: Large companies, public administration, and international organizations.

Profile: Data-Driven Decision Maker

Functions: Cover managerial functions of high responsibility in private and public companies with an international vocation with a strong technological component within it, using data analysis to guide strategic and operational decisions.

Skills: Baggage of theoretical knowledge of an economic-quantitative-IT nature to support organizational decisions and the development of economic institutions and companies.

Outlets: Small and medium-sized enterprises, large companies, and public administration.

Profile: Analyst of development projects or economic policies

Functions: Contribute to the formulation, monitoring, and analysis of development projects or economic policies.

Skills: The baggage of theoretical and operational notions in the economy, in the business management strategy, and in the economic policies that govern them.

Outlets: They operate in private or public companies in the industry, commerce, business services, personal and similar services and in international and/or governmental institutions.

Profile: Marketing Analytics Manager

Functions: The professions included in this category exercise functions of identification and supervision of decision-making processes of an operative nature in direct coordination with the company’s executive management.

Skills: Baggage of theoretical knowledge of an economic-quantitative-IT nature to support organizational decisions and the development of economic institutions and companies.

Outlets: Large companies.

Study Plan

The Master degree in Data Science and Economics is a genuinely multidisciplinary program, offering a well-balanced set of courses in data science and economics supported by several other courses. Students must acquire 120 ECTS to complete the program; among them, 24 credits are devoted to additional educational and research activities, for example, dissertation writing, research seminars, and elective courses.

The following are mandatory for all students:

First-year courses


  • Advanced Macroeconomics and Macroeconomics/12
  • Coding for Data Science and Data Management/12
  • Graph Theory, Discrete Mathematics and Optimization/12
  • Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/12
  • Micro-econometrics, Causal Inference and Time Series Econometrics/12

Total number of credits earned at the end of the first year/60

Second-year courses


  • Algorithms for Massive Data, Cloud and Distributed Computing/12
  • Cybersecurity and privacy, Preservation Techniques and Digital Security and Privacy/6
  • Cumulative number of credits earned after the second year’s mandatory courses/78

Three curricula


  • Economics/18
  • Business Innovation/18
  • Social Science/18
  • Cumulative number of credits earned after the second year’s mandatory courses/96
  • Elective courses/12
  • Internship/3
  • Master thesis/9

Total at the end of the programme/120

Program taught in:

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Last updated November 7, 2018
This course is Campus based
Start Date
Sep 2019
2 years
July 12, 2019
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