The program in Data Science and Scientific Computing is organized in two curricula: 

Each curriculum is further organized in several study plans, each corresponding to a specific application area, proposing a choice of specialization courses. 

Check out the Study Plan page for more information.


Select one or more paths

Software Development Methods

Objective: introducing concepts and techniques for collaborative development of large and complex software systems for industrial applications, including Java, software development lifecycle, best practices in software development as code testing, versioning, and design patterns.

Statistical Machine Learning

Objective: presenting advanced machine learning techniques, with a focus on deep learning and Bayesian methods.

Statistical Mechanics

Objective: providing methods and results of the elementary statistics mechanics in equilibrium.

Statistical Methods for Data Science

Objective: presenting the basic elements and principles of inferential statistics and statistical techniques for the analysis of complex data.

Stochastic Modelling and Simulation

Objective: introducing students to the fundamentals and practice of stochastic modeling, simulation of stochastic models and inference of parameters starting from observations, with a focus on scalability for large models.

Systems and Control Theory

Objective: providing advanced notions of the theory of dynamical systems both in continuous and in discrete time. Introduce to modern techniques for the design of complex control systems with particular reference to application contexts of engineering interest in the industrial field.

Theoretical Astrophysics

Objective: providing the elements of the physics of the stellar interior and of important radiative processes in astrophysics.