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.
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.
Objective: introducing the student to state of the art methods for the numerical simulation of partial differential equation.
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.
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.
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.
Objective: introducing techniques of analysis and statistical Bayesian inference.
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.
Objective: provide tools for the analysis of images and signals in the biomedical field.
Objective: introducing to the use of computational techniques to solve problems in fluid mechanics.
Objective: introducing to the dynamics of highly non-linear processes (turbulence) in fluid dynamics, and to the computational techniques used to solve such models.
Objective: providing fundamental tools and numerical algorithms for solving problems of classical physics and simple problems of quantum physics.
Objective: providing knowledge of the most important theoretical formalism used in quantum chemistry, and of the main computational methods, numerical algorithms, and software tools in the field of quantum chemistry.
Objective: introducing machine learning techniques for artificial vision and pattern recognition in sequential data.
Objective: introducing cyber-physical systems, with particular regard to modeling them with hybrid formalisms and the formal verification of their properties.
Objective: introducing students to computational management of data, in particular the characterization of an information system, data modeling, design and management of databases, including non-traditional ones (eg, unstructured documents, spatial data, biological data , multimedia data), to the fundamentals of distributed data and to methodologies and techniques for the management and analysis of big data.
Objective: providing the basic knowledge of fluid dynamics at the environmental scale and of tools for numerical modeling.
Objective: introducing the physical and mathematical principles of fluid dynamics.
Objective: providing a thorough and updated knowledge of cosmology issues related to the study of the formation of galaxies, clusters of galaxies and the structure of large-scale Universe in current cosmological models, through analytical, numerical, and statistical techniques for the evolution of disturbances in linear and nonlinear regime.
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.
Objective: presenting statistical methods and computational analysis techniques in genomics.
Objective: introducing advanced computational and statistical techniques for the analysis of clinical data.
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.
Objective: introducing to the foundations of modern cosmological theories.
Objective: introducing students to the principles of data analysis, to data mining, and to machine learning (supervised and unsupervised learning).
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.
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.
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.
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.
Objective: providing an introduction to numerical methods and techniques for the numerical solution of quantum mechanical problems, especially in atomic physics and condensed matter, with a practical approach.
Objective: providing a basic knowledge of the physical oceanography and how to integrate theoretical knowledge with experimental measurements.
Objective: providing students with practical information on how to design data models and data structures, to manage metadata to optimize access and research, and to become familiar with interoperability standards. The course will focus on the concept of open data, with efficiency for big data projects, and the concept of cloud as an infrastructure for data management and their processes.
Objective: providing the foundations of the modern approach to the control of dynamical systems, with particular reference to the treatment of uncertainty, structured and unstructured. Provide the main tools and methods for the analysis and synthesis of multiple-input-multiple-output control systems.
Objective: providing expertise on optimization techniques, with applications to industrial design.
Objective: providing students with the methodological, theoretical and practical tools to formulate linear programming models and combinatorial optimization problems and to solve them, even for high dimensionality problems, using appropriate optimization software.
Objective: providing students with the methodological, theoretical and practical tools to formulate linear programming models and combinatorial optimization problems and to solve them, even for high dimensionality problems, using appropriate optimization software.
Objective: provide knowledge of the fundamental properties of the dynamics and thermodynamics of the atmosphere, and the formulation and implementation of some simple analytical models of atmospheric dynamical systems.
Objective: providing the ability to understand the functioning and the internal structure of a molecular dynamics program. Being able to write code for molecular dynamics simulations and to analyze the output.
Objective: presenting statistical analysis techniques for social networks and other social and economic networks.
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.
Objective: presenting advanced machine learning techniques, with a focus on deep learning and Bayesian methods.
Objective: providing methods and results of the elementary statistics mechanics in equilibrium.
Objective: presenting the basic elements and principles of inferential statistics and statistical techniques for the analysis of complex data.
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.
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.
Objective: providing the elements of the physics of the stellar interior and of important radiative processes in astrophysics.