Dynamic Economic Modeling (a.a. 2023/24)

Experimental Methods and Behavioural Economics** (a.a. 2023/24)

Data-Driven Economic Analysis (a.a. 2023/24)

Functional and Topological Data Analysis (a.a. 2023/24)

The aim of the course is to introduce the main mathematical and statistical techniques that can be applied to analyse data that have an high geometrical complexity. 

Functional Data Analysis is applied to data that can be represented as functions, like for example time series, stochastic processes, density functions, etc. The functional data are here interpreted as patterns, and problems of classification, clustering, source of variation of the patterns are studied. 

Topological Data Analysis instead is focused on the analysis of the topological or geometrical structure of the data, like the presence of clusters, cavities (or regions with a low density), peaks (or regions with a high density), etc. In this framework data are represented still as functions, possibly multidimensional, or as graphs or networks.

Reinforcement learning (a.a. 2023/24)

Network Science (a.a. 2023/24)

Causal Inference and Policy Evaluation** (a.a. 2023/24)

Time Series and Forecasting** (a.a. 2023/24)

Statistical Theory and Mathematics (a.a. 2023/24)

The purpose of the course is that students learn the main mathematical, statistical and computational tools needed to approach a data science problem. The course serves mostly to refresh students' knowledge, and to ensure that all students have a common mathematical and statistical background.

Privacy, data protection, and massive data analysis in emerging scenarios (a.a. 2023/24)