Course Topics
M1: • Linear optimization techniques • Nonlinear optimization techniques • Discussion of combinatorial optimization problems • Multicriteria optimization and decision making • Decision making under uncertainty M2: Spatio-Temporal Data Analysis: Trend-surface estimation, spatial and temporal correlation, forecasting methods Robust Statistics & Outlier Detection: Data contamination and heavy tails, robust estimation and outlier analysis. Risk Modeling & Dependence Structures: Extreme value methods, multivariate risk assessment Applications: Finance and risk evaluation, environmental planning, policy and resource optimization

Teaching format
The course adopts a blended, student-centered approach that emphasises problem-based learning and active engagement. A portion of the lecture content is made available online in advance, allowing students to explore key concepts independently and at their own pace before attending class. This preparatory work enables in-person sessions to focus on the application of knowledge through real-world problems, collaborative activities, and guided discussions — fostering critical thinking and deeper learning. The course is fully aligned with the principles of the Italian Universities Digital Hub (EDUNEXT) initiative (https://edunext.eu), which promotes the integration of digital resources and active learning strategies within university teaching.

Educational objectives
Knowledge and understanding: The student will acquire knowledge of the analytical techniques and tools required to understand and quantitatively analyse economic and business phenomena in order to support decision-making processes. Knowledge of statistical inference, linear models and their generalisations, linear algebra, and optimisation techniques will be consolidated. In-depth knowledge of the main techniques of supervised and unsupervised statistical learning will be acquired, which are functional for the development of analysis and visualisation capabilities of economic and business data. Aapplying knowledge and understanding: Ability to apply and implement analysis techniques focusing on different types of datasets such as streaming data, tabular data, documents and images and analysis on joint datasets. Ability to apply supervised and unsupervised learning themes, and knowledge modelling, extraction, integration, analysis and exploitation; these skills are declined in various application domains of interest to companies and public and private entities Making judgements: Master graduates will have the ability to apply the acquired knowledge to interpret data in order to make managerial and operational decisions in a business context. Master's graduates will have the ability to apply the acquired knowledge to support processes related to production, management and risk promotion activities and investment choices through the organisation, analysis and interpretation of complex databases. Communication skills: Master's graduates will be able to communicate effectively in oral and written form the specialised contents of the individual disciplines, using different registers, depending on the recipients and the communicative and didactic purposes, and to evaluate the formative effects of their communication. Learning skills: "MSc graduates should be familiar with the tools of scientific research. They will also be able to make autonomous use of information technologies to carry out bibliographic research and investigations both for their own training and for further education. In addition, through the curricular teaching and the activities related to the preparation of the final thesis, they will be able to acquire the ability - to identify thematic links and to establish relationships between methods of analysis and application contexts; - to frame a new problem in a systematic manner and to implement appropriate analysis solutions; - to formulate general statistical-econometric models from the phenomena studied.

Assessment
The overall exam mark will be determined by the assessment of the two modules (M1+M2) M1: A written exam and a project presentation including an oral presentation. M2: Written exam: combination of multiple choice and essay questions. Project work: development of an individual project related to the methodologies studied, their implementation in statistical software, and their applications to empirical data.

Evaluation criteria
M1: The written exam of 1 hour counts 50%, the project 50% towards the final grade. Evaluation criteria are understanding of modeling features, capability of applying solution methods (only small scale for the written exam) problems and the capability to interpret/discuss the results w.r.t. economic/managerial decision making. M2: To pass the M2 module exam students must obtain a positive evaluation on both final exam (50% of the grade) and project (50% of the grade).