Objectives/Vision
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 “smarter” by leveraging data collected from increasingly smaller devices, anytime and anywhere. This data enables detailed recording and analysis of human, machine, and organizational behavior.
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.
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.
Career opportunities
The track prepares the next generation of “data architects” and “software and algorithm engineers” with deep computational, methodological, and modeling skills. Graduates will design and implement advanced data-intensive applications and services, by mastering cutting-edge technologies in the fields of Data Mining, Machine Learning, Artificial Intelligence, Data-intensive Platforms and Algorithms, and Complex System Modeling.
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.
Study plan
First year
Semester 1 |
CFU |
Semester 2 |
CFU |
| Algorithm engineering | 9 | Advanced databases | 9 |
| Data Mining | 9 | Bioinformatics | 6 |
| Computational mathematics for learning and data analysis | 9 | Parallel and distributed systems: paradigms and models | 9 |
| Information Retrieval | 6 | Group: BD elective 6 cfu | 6 |
| 33 | 30 |
Second year
Semester 3 |
CFU |
Semester 4 |
CFU |
| Group: BD elective 6 CFU | 6 | Group: BD elective 9 CFU | 9 |
| Group: BD elective 9 CFU | 9 | ||
| Group: free choice | 9 | Thesis | 24 |
| 24 | 33 |
Group: BD electives (9 CFU)
Digital Health lab (Sem. 2) (*)
Generative and deep learning (Sem. 2) (*)
Human languages technologies (Sem. 2) (not offered in the a.y. 25/26)
ICT risk assessment (Sem. 1)
Machine learning (Sem. 1)
Mobile and cyber physical systems (Sem. 2)
Peer to peer systems and blockchains (Sem. 2) (*)
Group: BD electives (6 CFU)
3D Geometric Modeling & Processing (Sem. 1)
Accelerated computing (Sem. 1) (*)
Advanced Laboratory of Complex Network Analysis (Sem. 1) (*)
Algorithmic Game Theory (Sem. 2)
Analysis (Sem. 1)
Competitive programming and contest (Sem. 1) (*)
Computational models for complex systems (Sem. 2)
Geospatial Analytics (Sem. 1)
ICT infrastructures (Sem. 2)
Introduction to Quantum Computing (Sem. 2)
Laboratory on ICT Startup Building (Sem. 2)
Scalable Distributed Computing (Sem. 1)
Scientific and large data visualization (Sem. 2)
(*) Courses offered only to new enrolled students.
Students enrolled before the academic year 2025/2026 can refer to the previous study plan (see the following linked document)