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

Advanced Mathematical Methods

Objectives: training students from different disciplines, such as applied mathematics, physics, engineering, to integrate theory and models in the study of some problems arising in applied sciences and which result in partial differential equation. Provide students with a mathematical background suitable to analyze them.

Advanced Numerical Analysis

Objective: introducing the student to state of the art methods for the numerical simulation of partial differential equation.

Advanced Programming and Algorithmic Design

Objective: providing advanced knowledge of both theoretical and practical programming in C / C ++ and Python, with particular regard to the principles of object oriented programming and best practices of software development (advanced use of version control systems, continuous integration, unit testing), and introducing the modern technology of algorithms development, in particular of parallel algorithms.

Algorithms for Massive Data

Objective: introducing to the main techniques for the design of algorithms and data structures to manipulate strings, trees and large graphs, in particular to compression techniques and randomization.

Applied Genomics

Objective: acquiring genomic knowledge and skills to use bioinformatics method in a rational and efficient way and to interpret the results correctly.


Objective: providing an overview, in the context of modern astronomy, to the various cosmic objects and give the basic principles necessary for the determination of their fundamental physical quantities.

Bayesian Statistics

Objective: introducing techniques of analysis and statistical Bayesian inference.

Big Data Bioinformatics

Objective: introducing the main algorithmic methods for the storage, compression and analysis of large amounts of biological data, with particular emphasis on the treatment of sequencing data produced with next generation sequencing technologies.


Objective: providing expertise on the motion of fluids inside the human body, especially in the cardiovascular system, with focus on the evaluation in a clinical settings.

Biomedical Signals and Bioimage Analysis

Objective: provide tools for the analysis of images and signals in the biomedical field.