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Data Science

The digitisation of information from physical and social systems has exponentially increased the production of data. New technologies have made interconnection, interaction, exchange and data collection easily accessible to governments, public administrations, small and medium-sized private companies, non-governmental organisations and individual citizens. This is driving a series of economic, social and political changes that require strong innovation in all areas of science and business. In the psychological and social sphere, Big Data has enormous potential to change the way in which it is possible to observe human behaviour, its lifestyles and its way of interacting with the surrounding world and their changing over time. In economic and managerial terms, the availability of information on individuals’ behaviours creates new channels of communication and interaction with consumers and all stakeholders, enabling them to develop new business models that companies adopt in an increasingly dynamic and complex market, and generate changes in economic policies and markets. In industry, the so-called Industry 4.0 or Smart Manufacturing, is in fact a Data-Driven Manufacturing. Thanks to improved performance and reduced cost of sensor and processing systems, information extracted from large amounts of data has become an essential factor in the development of industrial automation and advanced robotics. The availability of large amounts of data is also an inescapable factor in reducing energy costs and environmental impact, increasing productivity, optimising resources, and ultimately competing in the global economy.

The data, therefore, is no longer just information, but it is a resource with its own economic value that grows as it grows in its usability. At the same time, it increases the complexity of social and economic systems, whose government increasingly calls for real-time decision-making. These transformations are imposing a new professional figure, that of the Data Scientist, with strong transversal skills and capable of working in dynamic and multidisciplinary environments. His role is to analyse in a creative and innovative manner the data to give the decider, whether it is a manager or a researcher, useful information to define actions or draw strategies within the most complex organisations. The result of its analysis covers all departments of a company or administration, transforming the data into information that is understandable to the managers so that the latter can make informed decisions, sometimes anticipating trends and seizing opportunities of great economic, social, political or ethics.

The formation of the future Data Scientist, therefore, can only be multidisciplinary where computing, mathematics, statistics and social and economic sciences are carefully interwoven within a single training path. For these reasons, the departments of Mathematics, Sociology and Social Research, Information Engineering and Computer Science, Industrial Engineering, Psychology and Cognitive Sciences, Economics and Management, The Center for Mind/Brain Sciences, and the Fondazione Bruno Kessler (FBK) have decided to set up an interdepartmental Laurea Magistrale in Data Science.

Master degree

The Laurea Magistrale in Data Science will provide graduates with an in-depth knowledge of theoretical, methodological and technical in mathematics and statistics, computer science and domain expertise (such as for example: Social/Cognitive/Business/Industrial/Communication, etc.) that are the basis of data science with specific skills for the treatment and analysis of data in the field private, public and third sector. Graduates will therefore be able to deepen the themes advanced in the field of data science applied to the social, economic, cultural, political, psychological and industrial and productive systems and/or deepen the technical aspects of data science in the fields of mathematics, of statistics and of information technology.

During the training, great attention will be given to 'knowing how to do' and developing 'soft skills'. In fact, many of the courses and the lab activities will provide group design activities in interdisciplinary workshops and case studies with the direct involvement of the economic, social, or public administration directly involved. These skills will be further strengthened through external activities, such as training placements, at institutions or research institutes, laboratories, companies and public administrations, as well as stays at other Italian and European universities. The objective will be to foster both the development of interdisciplinary knowledge applied to concrete cases and the acquisition of those skills linked, inter alia, to relational, communicative, negotiating and organisational skills.

Course Structure

Instruction language

The courses of the Laurea Magistrale in Data Science are taught in English.

Goals

The Laurea Magistrale in Data Science aims at enabling its graduates to understand and analyze large data sets relating to individual and social behaviour, natural phenomena, or scientific pursuits.
The graduates will be able to offer evidence-based support to the decision making process at the executive level, in both the private and the public sector.

Curricula

The Corso di Laurea Magistrale in Data Science is organized into two curricula.
A student enrolls in one of the two curricula, according to her/his previous studies.


Curriculum A is meant for students who have taken a bachelor degree (Laurea) in one of the following areas: Computer Science, Mathematics, Physics, Statistics, Engineering.

Course Area
Data Mining Informatics
ICT and Social Science theory and models Sociology
ICT and cognitive psychology and models Psychology
Information, Knowledge and Service Management Economics
Intelligent Optimization for Data Science(*) Informatics
(*) Students should choose Intelligent Optimization for data Science in case they already took a course on introduction to machine learning in their career.

Curriculum B is meant for students who have taken a bachelor degree (Laurea) in one of the following areas: Sociology, Economics, Psychology.

Course Area
Mathematics for Data Science Mathematics
Programming Informatics
Algorithms and Data Structures Informatics
Computational Social Science Sociology

Common Courses
Course Area
Big Data Technologies Informatics
Professional English for Data Science English
Statistical Methods Statistics
Statistical Models Statistics
Data visualization lab Informatics
ICT and Law Privacy and Security Law
Introduction to machine learning Informatics

Show/Hide details about Curriculum A Show/Hide details about Curriculum B


    Students in both Curricula should additionally complete the following activities:
  • Elective course - II year (6 CFU): Students are required to choose 6 CFU from a list of elective courses which will be advertised in due time (see Regulations for further information).
  • Elective laboratories - II year (12 CFU): Students are required to choose 12 CFU from a list of elective laboratories which will be advertised in due time (see Regulations for further information).
  • Free-choice courses (12 CFU): Students are required to choose 12 free-choice credits among the courses offered by the University of Trento. The courses listed in the tables above are automatically approved. In all other cases, a personalized study plan must be completed and submitted to the commission for study plan examination.
  • Stage (9 CFU).
  • Thesis (18 CFU): The course of studies is concluded with the discussion of an original thesis, under guidance of a supervisor, providing 18 CFU.

Admission Requirements

To apply to the Laurea Magistrale in Data Science, an applicant shall fulfill a list of formal requirements and demonstrate a satisfactory level of personal qualifications.

    Applicants must have obtained:
  • at least 6 credits in Informatics (INF/*) or Information engineering (ING-INF/*)
  • at least 6 credits in Sociology (SPS/*) or Economics (SECS-P/*) or Psychology (M-PSI/*) or Law (IUS/*)
  • at least 6 credits in Mathematics (MAT/*) or Statistics (SECS-S/*)
  • at least further 24 credits in the above areas
    A Bachelor's degree requiring a three-year course of study or longer is mandatory. Additionally applicants should have basic knowledge on the following topics:
  • Mathematics (linear algebra and probability);
  • Computer Science (foundations of computer programming);
  • Basic theoretical and methodological notions of at least one of the following disciplines:
    • Social Science
    • Economics
    • Psychological Science
    The following information is required and shall be provided according to the instructions given in the web site:
  • a detailed study plan of the Bachelor's degree, including titles and syllabi of all the courses taken;
  • transcript of records from the University that issued the Bachelor's degree reporting, in Italian or English, the list of courses with title, credits and score obtained in each of them and the final score associated to the degree;
  • knowledge of English Language of at least B2 level or equivalent, certified by internationally recognized organizations or by the University that issued the Bachelor's degree or by the University of Trento;
  • a motivation statement, explaining the reason why the student is willing to apply to the Corso di Laurea Magistrale in Data Science, and what she or he expects from it;
  • a curriculum vitae and studiorum including personal experience and qualifications of the candidate besides those already stated in the academic record.

The level of personal qualifications of each applicant is evaluated by a Committee. In case of uncertainty on the actual content of courses attended by the candidate, the Committee may require him or her to supply the syllabi of the courses listed in transcript of records or the full diploma supplement. Failure to do so may be considered as insufficient information for the Committee to decide on the appropriate qualification of the candidate.
The Committee requires a personal interview (possibly remotely) with the applicants, to better evaluate their curriculum. The interview can include questions on the main topics studied in the respective applicant’s Bachelor’s Degree.

Summer School

Between the end of July and the beginning of August, a summer school will be organized in order to facilitate access even for those who have only partially the necessary credits. In particular, a two week full-time period of short lessons (18hours - 3CFUs) is foreseen in the following three areas: Computer Science, Mathematics/Statistics and Social Sciences. Attendance is compulsory and the student may not attend more than four courses. At the end of the period, the student will be recognized CFUs in each of the areas of attendance after passing a profit exam. The summer school will be held in Trento and will have a registration fee. There are no prerequisites for participating in the summer school.

Provisional fees of the courses

1 course Euro 100
2 courses Euro 140
3 courses Euro 160

This information is subject to change after the final approval of the competent bodies.

In the news

Prof. Bison has been interviewed by the national newspaper La Repubblica about the importance, demand and role of the data scientist.