[DS] Organisation and contacts

The Master’s Degree in Data Science is characterized by an interdisciplinary educational offering that integrates contributions from engineering, computer science, statistics, economics, and organizational sciences, together with domain-specific knowledge in the main application areas of Data Science.
In particular, the program provides the professional knowledge necessary for developing technologies for the collection, management, processing, and analysis of big data, and for transforming these data into information that supports knowledge discovery and decision-making processes in innovative business and social sectors.
The program has a two-year duration and includes an initial core of 39 ECTS credits in key disciplinary areas, designed to provide the statistical, engineering, and computer science foundations required for the development of software tools and infrastructures for data collection, processing, and organization, as well as for the mathematical and statistical modeling necessary for data analysis. At least 10 of these 39 ECTS are devoted to laboratory activities.
These core courses are mandatory for all students and are divided as follows:

• 27 ECTS in computer science technologies, and
• 12 ECTS in statistical disciplines.

Students can then select up to 30 ECTS from specialized elective courses in related disciplinary areas. At least 6 ECTS must be chosen from human, social, legal, or economic sciences.
These courses aim to shape a professional profile that combines engineering and computer science skills with statistical, managerial, economic, and legal competences, developed alongside a strong understanding of the economic, social, and organizational contexts in which Data Science methodologies are applied.
The curriculum also includes 3 ECTS for Other Educational Activities, such as internships in companies or participation in thematic training camps, as well as 12 ECTS in related fields and 12 ECTS of free-choice courses.
There are no mandatory attendance requirements, except for laboratory and practical activities.
All courses are taught in English.
Learning outcomes are assessed through midterm evaluations, group project discussions, and individual written assignments, as well as traditional exams.
The program enables graduates in Data Science to find employment in small and medium-sized enterprises, large companies, public administration, local government bodies, public and private research institutes, and non-profit organizations. Graduates may also choose to pursue Ph.D. programs or second-level Master’s degrees as a continuation of their studies.
Admission to the Master’s Degree requires fulfillment of the curricular requirements (RC) and demonstration of adequate personal preparation (APP), including verification of English language proficiency.
Curricular Requirements (RC)
Applicants must satisfy all of the following:

• (RC-a) Possession of a three-year university degree or an equivalent qualification obtained abroad and deemed suitable.
• (RC-b) Acquisition of at least 90 ECTS credits in total across the following disciplinary areas:
  • Mathematical and computer sciences: MAT/*, INF/01
  • Physical sciences: FIS/*
  • Economic and statistical sciences: SECS-P/, SECS-S/
  • Industrial and information engineering: ING-IND/, ING-INF/
  • Biological sciences: BIO/*
  • Legal sciences: IUS/*
  • Earth sciences: GEO/*
  • Civil and environmental engineering: ICAR/*
  • Logic and philosophy of science: M-FIL/02
• (RC-c) English language proficiency at B2 level or higher.

These requirements are designed to allow access to the program for students holding Bachelor’s degrees in the following Italian degree classes (or their equivalents under Ministerial Decree 509/1999):
L-8 (Information Engineering), L-31 (Computer Science and Technologies), L-41 (Statistics), as well as L-18 (Economics and Business Management), L-30 (Physical Sciences), L-33 (Economics), and L-35 (Mathematical Sciences).
Verification of the admission requirements, particularly the adequacy of personal preparation, will be carried out by a dedicated Committee appointed by the Degree Program Council.
Verification of Curricular Requirements
The applicant must simultaneously satisfy conditions (RC-a), (RC-b), and (RC-c).
Condition (RC-c) is fulfilled by presenting a B2-level English certificate or documentation showing B2-level English course credits (including pass/fail “idoneità” exams) obtained during previous studies.
If no certification or credits are available, applicants must pass an English proficiency interview.
Verification of Adequate Personal Preparation (APP)
Adequate personal preparation is evaluated through two aspects:

• (APP-a) Results and relevance of previous academic studies.
• (APP-b) Knowledge of Mathematics, Probability, and Computer Science.

For (APP-a), the Committee will assess:

• The final grade obtained in the Bachelor’s degree and the average of grades, paying particular attention to marks achieved in Mathematics, Probability, and Computer Science;
• The relevance of the previous degree program to the Data Science curriculum.

For (APP-b), the Committee will evaluate knowledge in the following areas:

• (APP-b1) Mathematics: Differential and integral calculus for functions of one or more real variables; basic concepts of linear algebra and analytic geometry in the plane and space.
• (APP-b2) Probability: Random variables, distributions, expected values; main random variable models; convergence of sequences of random variables.
• (APP-b3) Computer Science: Programming principles, basics of object-oriented design; at least one programming language among C, C++, C#, Java, Python.

The Committee will automatically consider requirement (APP-b) satisfied for students who have obtained, with an average grade above 24/30, at least:

• 12 ECTS in MAT/03 (Geometry) and/or MAT/05 (Mathematical Analysis) and/or MAT/09 (Operations Research),
• 6 ECTS in MAT/06 (Probability),
• 6 ECTS in INF/01 (Computer Science) and/or ING-INF/05 (Information Processing Systems).

If only one of the knowledge areas (APP-b1), (APP-b2), or (APP-b3) is missing, students must take a test and/or oral interview to assess and complete the missing area, in order to fully satisfy the adequate personal preparation (APP-b) requirement.