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Home Graduate Programs 2-year Graduate Degrees in English Statistical and Actuarial Sciences


Statistical and Actuarial Sciences

Campus School Duration Total ECTS Edition n.
Milano, Italy Banking, Finance and Insurance Sciences | Economics September 2018 - September 2020 120 2


Statistical and Actuarial Sciences

Applications for the academic year 2017/2018 to the M.Sc. in Statistical and Actuarial Sciences are now closed.

Applications for the academic year 2018/2019 will open in Fall; contact us if you wish to receive updates on application openings, scholarships and new deadlines!

The M.Sc. in Statistical and Actuarial Sciences is a joint program designed by two schools, the School of Banking, Finance and Insurance Sciences and the School of Economics.

Each school has a dedicated track: 

  1. The Actuarial sciences for insurance track, which prepares students to have direct access to the state actuary examination, is in line with the international standards regarding the actuary sciences (the core component of the program is in line with the recommendations by both the Actuarial Association of Europe and the International Actuarial Association).
  2. The Data analytics for business and economics track, is designed in accordance to the standards of international curricula in the field of data science.


Learning objectives

Track 1 - Actuarial sciences for insurance program:

  1. strong foundation of knowledge in statistic methodology and its applications to the fields of economics, economics-management, finance, demographics, social, insurance and social security;
  2. deep knowledge of mathematical models, in particular those applied to economic and business sciences;
  3. deep knowledge of quantitative models applied to risk management;
  4. command of logic-conceptual and methodological tools for project design and the running of surveys for the analysis and assessment of complex systems associated to the economy, production, the market, to insurance problems, to the environment, with particular reference to the occurrence of damaging events;
  5. corresponding capabilities to build models and explain and forecast phenomena that are subject of study and establish the application and validity with appropriate data analysis, and a resulting in a highly qualified practical ability in the field of quantitative analysis of economic, business, socio-demographic phenomena and of financial problems tied to social security and insurance.

Track 2 - Data analytics for business and economics program:

  1. finding data, by way of sample analysis or specific experimental designs;
  2. display, modeling and data analysis;
  3. assessment and presentation of results, with possible original solutions to support complex decision-making processes of today’s company management across sectors, such as digital marketing or simulation in complex micro and macro-economic scenarios;
  4. deep knowledge of the foundations and applications of methodologies of statistic, probabilistic, mathematic and computational nature, that allow for the building of inferential and forecasting models that may be used for exploratory reasons, to confirm prior assessments or simply to support decision-making processes. In fact, in order to confront the enormous flow of information which is tied to the digital revolution that marks the complex world of companies and society, it is necessary to provide competences for large scale and complex data analysis to comprehend opportunities for commercial and economic growth. For this reason, the graduate will need to have robust computational and methodological skills that allow for autonomous analysis and assessment. In particular, the graduate student will need to know the modern methodologies of statistic regularization, statistical learning, data mining and data visualization. Furthermore, the data scientist identifies him/her-self more like a scientist discoverer of novelty inherent to data rather than an analyst who generally applies protocols although sophisticated;
  5. correlated ability to identify and manage, in an efficient manner, the vast quantity of available data, whether in traditional databases that are so called structured and that are typical of companies or institutional organizations, such as national or supranational institutes of statistics and central banks, or in non-structured databases, available through the internet, via the common open-source packages or through information tools that are commercial in nature at research companies or entities;
  6. correlated ability to use problem analysis approaches that are corporate and economic in nature, acquired through an interdisciplinary method, and to communicate the results through modern techniques to display data.


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