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Data Science

The increasing digitization of physical and social systems is exponentially increasing 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 organizations and individual citizens. This is driving a series of relevant economic, social and political changes that require strong innovation in all areas of science and business.

In the psychological and social sphere, Big Data have enormous potential to redesign the ways in which we observe how human behaviors, lifestyles and interactions unfold and evolve over time.

In the public sector, unprecedented data availability raises the complexity of social and economic systems, whose government and governance increasingly call for real-time decision-making.

In the economic and managerial domains, increased availability of information on individual behaviors creates new channels of communication and interaction between companies, consumers, and, more broadly, all stakeholders. In turn, this triggers the development of new business models that fit an increasingly dynamic and complex context thus generating 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 crucial for the development of industrial automation and advanced robotics.

Increased availability of massive amounts of data is also of paramount importance for reducing energy costs and environmental impact, increasing productivity, optimizing resources, and, ultimately, competing in the global economy balancing profitability and sustainability. Therefore, data are no longer a mere precondition to create useful information. Rather, they are a resource with its own economic value – a value that grows together with their usability.

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. The role of a Data Scientist is to analyze data in creative, innovative and conscious ways and thus to provide to decision-makers, whether they are managers, researchers, or representatives of the institutions, the most useful information to define action lines and draw strategies to cope with increased diversity, dynamics and complexity. Thus, a Data Scientist works transversally to all departments of a company, administration or organization transforming data into information that is understandable and, more importantly, useful to make informed decisions, anticipate trends and seize growing opportunities in all areas.

To become a true data-savvy manager and an agent of innovation, a future Data Scientist needs to be trained in a highly multidisciplinary fashion and along a unique educational path wherein computing, mathematics, statistics and social and economic sciences are carefully interwoven. It is precisely with the aim of offering such a peculiar training and educational environment that, at the University of Trento, 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 MSc in Data Science (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 23rd of July and 4 th of August, a summer school will offer the opportunity to acquire valuable credits to qualify for the enrolment into the Laurea Magistrale in Data Science, particularly for those who have only some of the necessary credits or just want to take a refresher course in: informatics and/or mathematics/statistics and/or socio-psych-economics area. Over a two-week period, a set of full-time short courses (18hours course - 3CFUs) will be offered in the following three areas: (a) Mathematics/Statistics (MAT/, SECS-S/); (b) Computer Science (INF/* ING-INF/); (c) Economical, Psychological, and Sociological Science (SPS/, SECS-P/, M-PSI/). Attendance is compulsory, and the student may not attend more than four courses (12 CFU), two during the first week e two during the second week. At the end of the period, the student will be recognized CFUs in each of the areas of attendance upon passing a final exam. The summer school will take place in Trento and will have a registration fee. There are no prerequisites for participating in the summer school. The courses will be held in Italian.

Timetable

The timetable of the school is available here

Provisional fees of the courses

1 course Euro 100
2 courses Euro 140
3 courses Euro 160
4 courses Euro 180

Application are now open!

Istruction for application are available here.

Week 1

23 July – 28 July

Module 1: MATHEMATICS & STATISTICS

(Hours 18, 3 CFU, (MAT/, SECS-S/)

Title: Linear algebra

Mario Lauria (University of Trento)

Syllabus:

  • Functions: What is a function. General notions on functions. The main elementary functions. The equation of the line. Straight lines through a point. Straight lines through two points. Intersection of two lines. Parallel lines. The parametric equation of the line. The parable. The equilateral hyperbola. The power functions. The exponential function. The logarithm functions. The concept of infinity. [§ 3 e 4]
  • Derivatives: Definition of derivative. Calculation of some derivatives. Rules of derivation. Search for the maximum and minimum points of a function. Optimality problems. Convexity and concavity of a function. Flexion points. Study of a function. [§ 7 e 8]
  • Antiderivatives: The immediate and almost immediate antiderivatives. Integration by parts. Integration by substitution. Integral defined for continuous functions. Geometric interpretation of the integral. Calculation of a definite integral. Generalized (or improper) integrals. [§ 9 e 10]

Suggested textbook

“Matematica per le scienze” di A. Guerraggio.

Module 2: COMPUTER SCIENCE

(Hours 18, 3 CFU, INF/01):

Title: Foundations of Programming in Python

Davide Poggiali (University of Padua)

Syllabus:

  1. What is a program, variables, expressions and statements; 2. Functions, conditionals and recursion; 3. Functions with return value, iteration; 4. Strings; 5. Dictionaries, tuples; 6. Case study: data structure selection, files.

Module 3: ECONOMIC, PSYCHOLOGICAL, AND SOCIOLOGICAL SCIENCE

(Hours 18, 3 CFU, SPS/, SECS-P/, M-PSI/*):

Title: An Introduction

Elena Pavan (University of Trento), Luigi Lombardi (University of Trento) and Roberto Gabriele (University of Trento)

Syllabus:

  • Introduction to economics (hours 6): Individual decisions and system behaviour: concepts and models; Production costs: definitions and interpretative models; The market: supply and demand, models; Presentation of examples on the topics covered.
  • Introduction to cognitive representations (hours 6): A selection of some elementary concepts about cognitive psychology and cognitive architectures are presented in a simple and integrated fashion. Basic definitions for psychological dimensions such as, for example, perception, motion, and action as well as learning, memory, judgement and decision making are gently illustrated within a cognitive approach.
  • Introduction to sociology (hours 6): This module will discuss some of the main research tradition of sociological research with an emphasis on analytical and structural sociology that aims at understanding establishing and explaining probabilistic regularities in human populations. We will discuss some of the main object of sociological research and how they have been operationalized into empirical research including the challenges involved.

Suggested textbook

Economics

  • C.W.L. Hill, (2011) International Business: Competing in the global marketplace, (8th ed.), NY
  • Baldwin, R. E., & Evenett, S. J. (2015). Value creation and Trade in 21st Century Manufacturing, Journal of Regional Science, 55(1), 31-50.
  • Chesbrough E. (2003) Open Innovation, Harvard Business School Press, Boston, Massachusetts, Principles of Economics, Mankiw G. (2014) 7 ed., South-Western Pub

    Psychology

  • M. W. Eysenck & M. T. Keane (2015). Cognitive Psychology A Student’s Handbook, 7th Edition. Psychology Press.

    Sociology

  • Ackland, R. (2013). Web social science: Concepts, data and tools for social scientists in the digital age. London: SAGE Publications.
  • Goldthorpe, J. H. (2016). Sociology as a population science. Cambridge: Cambridge University Press.
  • Marres, N. (2017). Digital sociology: The reinvention of social research. Cambridge: Polity Press.
  • Steuer, M. D. (2011). The scientific study of society. New York: Springer.

Week 2

30 July - 04 August

Module 4: MATHEMATICS & STATISTICS

(Hours 18, 3 CFU, (MAT/, SECS-S/)

Title: Introduction to Statistics

Diego Giuliani (University of Trento) and Matteo Tomaselli (University of Trento)

Syllabus:

  • Data: statistical units, variables, and measurement scales. Graphical methods, tabular tools, and numerical measures for univariate and bivariate descriptive analysis.
  • Introduction to probability: basic concepts and definitions, random variables, and probability distributions. Sampling and sampling distributions. Introduction to the inferential statistics: interval estimation and hypothesis test.

Suggested textbook

  • Anderson D., Sweeney D., Williams T. (2010) Statistica per le analisi economiCo-aziendali, Apogeo.
  • Agresti A., Franklin C. (2016) Statistica. L’arte e la scienza di imparare dai dati, Pearson.

Module 5: COMPUTER SCIENCE

(Hours 18, 3 CFU, INF/01)

Title: Foundations of Algorithms in Python

Davide Poggiali (University of Padua) and Maurizio Napolitano (Fondazione Bruno Kessler)

Syllabus:

  1. Search on lists, orderings; 2. Dictionaries, lists of lists, introduction to matrices; 3. Matrices with numpy; 4. Tree data structures, json and xml formats; 5. Operations on sequences, list comprehensions; 6. Introduction to databases

Module 6: ECONOMIC, PSYCHOLOGICAL, AND SOCIOLOGICAL SCIENCE

(Hours 18, 3 CFU, SPS/, SECS-P/, M-PSI/*)

Title: Modelling and methodology

Giuseppe Veltri (University of Trento), Luigi Lombardi (University of Trento) and Roberto Gabriele (University of Trento)

Syllabus:

  • Introduction to management (hours 6): Porter’s scheme: Competitive dynamics and strategies; Open and closed innovation: comparison paradigms; the value chain: company boundaries and international dynamics; Discussion of some cases on the concepts treated
  • Introduction to cognitive methodology (hours 6): The three major methodological frameworks to the study of cognition are illustrated: a) the experimental paradigm b) the correlational approach c) the modelling perspective. For each of these three methodological viewpoints, a simple case study will be used as a practical guiding example, which will also highlight some connections between these approaches and data science problem representations.
  • Introduction to sociological methodology (hours 6): We will cover the main epistemological and methodological aspects of research with social data using quantitative, qualitative and mixed methods. We will focus more on quantitative methods, in particular about the nature of data available, the construction and use of valid research instruments and the different methods of analysis applied to social data.

Suggested textbook

Economics

  • C.W.L. Hill, (2011) International Business: Competing in the global marketplace, (8th ed.), NY
  • Baldwin, R. E., & Evenett, S. J. (2015). Value creation and Trade in 21st Century Manufacturing, Journal of Regional Science, 55(1), 31-50.
  • Chesbrough E. (2003) Open Innovation, Harvard Business School Press, Boston, Massachusetts, Principles of Economics, Mankiw G. (2014) 7 ed., South-Western Pub

    Psychology

  • M. W. Eysenck & M. T. Keane (2015). Cognitive Psychology A Student’s Handbook, 7th Edition. Psychology Press.

    Sociology

  • Ackland, R. (2013). Web social science: Concepts, data and tools for social scientists in the digital age. London: SAGE Publications.
  • Goldthorpe, J. H. (2016). Sociology as a population science. Cambridge: Cambridge University Press.
  • Marres, N. (2017). Digital sociology: The reinvention of social research. Cambridge: Polity Press.
  • Steuer, M. D. (2011). The scientific study of society. New York: Springer.

In the news

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