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Public interest in Data Science and Big Data is multiplying as data-driven decision-making becomes increasingly visible in everyday life. Society has rapidly shifted from predominantly analog to digital. Companies, organizations, and individuals are constantly connected. The Internet of Things (IoT) significantly contributes to this expansion: homes, cars, factories, and cities are becoming \u201csmarter\u201d by leveraging data collected from increasingly smaller devices, anytime and anywhere. This data enables detailed recording and analysis of human, machine, and organizational behavior.<\/span><\/p>\n While big data holds great promise for wellbeing, social development, and the economy, they are generated at a pace far exceeding current computational capacities. Moreover, many analytical tools, models, and skills to make sense of this data remain lacking. We face the challenge of developing the algorithms, models, methodologies, tools, and competencies to acquire, store, process, analyze, search, and mine these vast datasets and to extract competitive and unexpected knowledge using data-driven approaches. Progress in these areas will have a profound impact on scientific, business, and social applications across diverse fields, including web search, social networks, banking, manufacturing (Industry 4.0\/5.0), transportation, healthcare, genomics, policy-making, education, retail, and more.<\/span><\/p>\n There is a critical shortage of professionals skilled in transforming vast data into actionable insights, backed by strong computer science foundations. Demand for such experts spans global tech giants, telecoms, retailers, energy and insurance firms, statistical institutes, bioinformatics companies, and countless startups leveraging big data.<\/span><\/p>\n The track prepares the next generation of \u201cdata architects\u201d and \u201csoftware and algorithm engineers\u201d with deep computational, methodological, and modeling skills. Graduates will design and implement advanced data-intensive applications and services<\/span>,<\/span> by mastering cutting-edge technologies in the fields of Data Mining, Machine Learning, Artificial Intelligence, Data-intensive Platforms and Algorithms, and Complex System Modeling.<\/span><\/p>\n Graduates will be well prepared for careers in top-tier tech companies (both national and international) or to pursue Ph.D. programs in Computer Science, Artificial Intelligence, or related fields.<\/span><\/p>\n Digital Health lab (Sem. 2) (*) 3D Geometric Modeling & Processing (Sem. 1) (*) Courses offered only to new enrolled students.<\/p>\n Students enrolled before the academic year 2025\/2026 may refer to the previous\u00a0 rules.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Objectives\/Vision Public interest in Data Science and Big Data is multiplying as data-driven decision-making becomes increasingly visible in everyday life.…<\/p>\nCareer opportunities<\/h2>\n
<\/div>\nStudy plan<\/h2>\n
First year<\/h3>\n
\n\n
\n \n Semester 1<\/h3>\n<\/td>\n
\n CFU<\/h3>\n<\/td>\n
\n Semester 2<\/h3>\n<\/td>\n
\n CFU<\/h3>\n<\/td>\n<\/tr>\n
\n Algorithm engineering<\/td>\n 9<\/td>\n Advanced databases<\/td>\n 9<\/td>\n<\/tr>\n \n Data Mining<\/td>\n 9<\/td>\n Bioinformatics<\/td>\n 6<\/td>\n<\/tr>\n \n Computational mathematics for learning and data analysis<\/td>\n 9<\/td>\n Parallel and distributed systems: paradigms and models<\/td>\n 9<\/td>\n<\/tr>\n \n Information Retrieval<\/td>\n 6<\/td>\n Group: BD elective 6 cfu<\/a><\/td>\n 6<\/td>\n<\/tr>\n \n <\/td>\n 33<\/b><\/td>\n <\/td>\n 30<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Second year<\/h3>\n
\n\n
\n \n Semester 3<\/h3>\n<\/td>\n
\n CFU<\/h3>\n<\/td>\n
\n Semester 4<\/h3>\n<\/td>\n
\n CFU<\/h3>\n<\/td>\n<\/tr>\n
\n Group: BD elective 6 CFU<\/a><\/td>\n 6<\/td>\n Group: BD elective 9 CFU<\/a><\/td>\n 9<\/td>\n<\/tr>\n \n Group: BD elective 9 CFU<\/td>\n 9<\/td>\n <\/td>\n <\/td>\n<\/tr>\n \n Group: free choice<\/td>\n 9<\/td>\n Thesis<\/td>\n 24<\/td>\n<\/tr>\n \n <\/td>\n 24<\/b><\/td>\n <\/td>\n 33<\/b><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Group: BD electives (9 CFU)<\/h3>\n
\nGenerative and deep learning (Sem. 2) (*)
\nHuman languages technologies (Sem. 2) (not offered in the a.y. 25\/26)
\nICT risk assessment (Sem. 1)
\nMachine learning (Sem. 1)
\nMobile and cyber physical systems (Sem. 2)
\nPeer to peer systems and blockchains (Sem. 2) (*)<\/p>\nGroup: BD electives (6 CFU)<\/h3>\n
\nAccelerated computing (Sem. 1) (*)
\nAdvanced Laboratory of Complex Network Analysis (Sem. 1) (*)
\nAlgorithmic Game Theory (Sem. 2)
\nSocial and ethical issues in computer technology (sem. 2)
\nCompetitive programming and contest (Sem. 1) (*)
\nComputational models for complex systems (Sem. 2)
\nGeospatial Analytics (Sem. 1)
\nICT infrastructures (Sem. 2)
\nIntroduction to Quantum Computing (Sem. 2)
\nLaboratory on ICT Startup Building (Sem. 2)
\nScalable Distributed Computing (Sem. 1)
\nScientific and large data visualization (Sem. 2)<\/p>\n