Knowledge and comprehension abilities
Statistical Sciences graduates:
- have a solid mathematical and probabilistic foundation;
- have in-depth knowledge of the statistical methodology, both from a descriptive and a modelling perspective;
- know the methodological fundamentals of the main parametric and nonparametric techniques of statistical inference;
- know the main statistical methods for multivariate data analysis, even high-dimensional data (big data);
- possess an advanced knowledge of complex design planning methods;
- possess a solid basis in the information technology field that allows them to manage databases;
- are aware of the problems related to the management and production of official statistics;
- possess extensive knowledge in the social, demographic and bio-health fields.
The knowledge and comprehension abilities listed above are gained through lectures, exercises and analyses of case studies. Any homework required during classes together with independent study provide students with the tools to learn and acquire solid knowledge bases. Learning outcomes are verified mainly through oral and written exams, project work or reports, either during courses or at the end.
Capacity to apply knowledge and comprehension
Statistical Sciences graduates:
- are able to formalize cognitive problems and phenomena through statistical and probabilistic models;
- are able to make punctual and interval estimates of quantity of interest and to check hypotheses;
- are able to identify the most appropriate and consistent statistical methods for processing information;
- are able to design complex investigations and statistical studies in various phenomenal areas;
- can handle large amounts of data and high-dimensional data;
- know how to produce official indicators and statistics;
- are able to analyse official statistics at a national and international level;
- are able to construct experimental designs for case-control studies;
- are able to construct analysis models and make predictions in socio-demographic and bio-health contexts;
- are able to extract information to support decision-making processes.
The acquisition of the necessary skills to apply the above-mentioned knowledge and understanding takes place through a teaching approach shared by all the courses in statistics, mathematics and demography (both compulsory and electives ones). This approach provides for theoretical training accompanied by laboratories, case studies and applications, individual or group work that stimulate active participation, a purposeful attitude, critical elaboration skills, and the ability to interpret and communicate results. Studying independently is fundamental to expand and elaborate knowledge, and it is guided by homework and exercises. Internships serve an additional tool for a concrete self-assessment of the level of knowledge acquired during the master's programme. Finally, the preparation of the final thesis is a fundamental step for the re-elaboration of knowledge and the development of critical, interpretative and communicative skills. Learning outcomes are verified mainly through written and oral exams, project work and reports. In any traineeship activities, verification takes place via the presentation of a final report.
