The Faculty of Economics together with the Faculty of Banking, Finance and Insurance Sciences offers its learning, organisational, and research resources for the launch of this interfaculty Master of Science Programme.
The course is structured in two curricula:
- Actuarial sciences for insurance;
- Data analytics for business and economics.
The learning goals for the Master of Science in Statistical and actuarial sciences (which belongs to Course Class LM-83 and is taught in English) include the following competencies, knowledge and abilities:
For the profile, Actuarial Sciences for Insurance, which allows a direct access to the actuarial exam, in line with the international courses in actuarial sciences (cf. International Core Syllabus of both the Actuarial Association of Europe and International Actuarial Association):
- strong knowledge of statistical methodology and its applications in the fields of economics, economic-management, finance, demography, sociology, insurance and social security;
- deep knowledge of mathematical models, specifically probability models to apply to finance and actuarial phenomena as well as economic and corporate sciences;
- deep knowledge of quantitative models in the area of risk management;
- mastery of logic, conceptual and methodological tools for planning and executing research for the analysis and evaluation of complex systems linked to economies, production, markets, insurance problems and the environment, with a specific reference to the occurrence of damaging events;
- corresponding ability to build models that explain and foresee phenomena being studied and establish their applicability and validity with data analysis, and therefore a highly qualifying operating ability in the field of quantitative analysis of economic, corporate, socio-demographic and financial problems related to social security and insurance.
For the Profile Data analytics for Business and Economics in line with the international courses in Data Science:
- data collection, also through samples or specific experimental designs;
- visualization, modelling and analysis of data sets;
- evaluation and presentation of results, together with original solutions to support complex decision-making processes of the management of companies in highly different areas, from digital marketing to macro and micro economic simulations in complex systems;
- deep knowledge of the foundations and applications of probabilistic, mathematical, statistical and computational methodologies, which enable the construction of inferential and forecasting models with the purpose to explore, confirm and support the decision-making processes. To face the huge flow of information that characterises the business world and society as a whole as a result of the IT revolution, it is important to provide students with the ability to analyse complex and big data and to enable them to seize opportunities for commercial and economic development. To this end graduates should have strong methodological and computational foundations that enable them to develop analyses and evaluations autonomously. In particular graduates should know the modern methods of computational statistics, statistical regularization, statistical learning, data mining and data visualization. Specifically, a data scientist should be more similar to a scientist who finds novelties inside data, rather than to an analyst who merely applies potentially sophisticated procedures. Strong methodological training (especially in the statistical, probabilistic and computational fields) along with technical know-how in the economic and business world, will allow students to identify effective responses and solutions in the different application contexts.