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

Foundations of High Performance Computing

Objective: introducing students to modern architectures for high performance computing. Students will learn how to properly test such architectures (computing power, bandwidth, latency, energy efficiency). Leveraging these skills, students will be introduced to the parallel programming based on MPI protocols (Message Passing Interface) and multi-threading with OpenMP.

Genomic Data Analytics

Objective: presenting statistical methods and computational analysis techniques in genomics.

Health Data Analytics

Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.

Information Retrieval

Objective: presenting the most important conceptual  aspects of information retrieval systems, with particular attention to search engines on the Web,  discussing basic arguments, current lines of research, and future trends.

Introduction to Cosmology

Objective: introducing to the foundations of modern cosmological theories. 

Machine Learning and Data Analytics

Objective: introducing students to the principles of data analysis, to data mining, and to machine learning (supervised and unsupervised learning).

Management of Health Data

Objective: providing expertise for the management of clinical and biomedical data from computerized medical records, through the methods of health information technology and of process modeling.

Molecular Simulation

Objective: introducing the computational techniques used in molecular modeling and simulation, and illustrating how these techniques can be employed to describe and/ or predict chemical, physical and biological phenomena.

Network Science

Objective: You will learn how to organize, transform, analyse and visualize data, with a focus on the relational data model, and a detour to semistructured data. You will learn the fundamentals of data science using R environment.

Numerical Analysis

Objective: providing numerical analysis tools for scientific computing, with particular attention to linear algebra, polynomial approximation, numerical integration, numerical solution of ordinary differential equations and partial differential equations, approximation of eigenvalues and eigenvectors.