Objectives/Vision
Public interest in data science and big data is mounting as data-driven decision making becomes visible in everyday life. Society shifted from being predominantly “analog” to “digital” in just a few years. Companies, organizations, and people are “always on”. The Internet of Things (IoT) is contributing significantly to this expansion: Our homes, cars, factories, and cities are getting “smarter” by exploiting the data that is collected from smaller and smaller devices at any time, at any place and about everything. This makes it possible to record and analyze the behavior of people, machines, and organizations.
While these big data are expected to foster wellbeing, social development and the economy, they are produced at a rate that is far greater than that of the computing power to process them and we miss most of the analytical tools, models and skills to make them usable. This means that we are facing the challenge of providing algorithms, models, methodologies, tools, technologies and competences to acquire, store, process, analyze, search and mine these data. Developments in these areas will have significant impacts onto scientific, business and social applications in many diverse fields such as web search, online social networks, banking, manufacturing (industry 4.0), mobility and transportation, health care and genomics, policy making, education, retail, and so on. These developments are also rapidly changing the way we do business, socialize, conduct research, and govern society.
Career opportunities
We are experiencing a severe shortage of scientists and professionals able to master the models, the algorithms and the technologies to turn data into meaningful information, with a strong computer science background. So there is no surprise that small and global companies are seeking for professionals with these skills, including software companies such as IBM, Microsoft, SAS, etc.; data-intensive companies, such as Google, Facebook, Bloomberg, VISA; telecom providers such as Vodafone, TIM, Wind, Huawei; telematics providers such as Octo, Navionics; retail companies such as Coop, Amazon, Ebay; energy providers such as ENEL, ENI; insurance companies such as Generali, Unipol; statistics institutes such as ISTAT and Eurostat; bioinformatics and genomic companies, such as Illumina; up to the huge universe of small and medium enterprises as well as start-ups that are developing products deploying Big Data in every business domain.
Given these premises, the curriculum has been designed with the aim of educating the next generation of “data architects” and “software and algorithm engineers” endowed with deep computational, methodological and modeling skills that will allow them to design and implement the future data-intensive algorithms, tools and platforms, as well as master the cutting-edge technologies for big-data analytics, such as Hadoop, Spark, together with the mainstream tools for data and text mining, machine learning, artificial intelligence, complex system modeling and mining spurring from e.g. genomic, web, business, industrial or social applications.
This master course also provides a solid background for a Ph.D. program in Computer Science or an equivalent degree.
Study plan
First year
Semester 1 |
CFU |
Semester 2 |
CFU |
| Algorithm engineering | 9 | Advanced databases | 9 |
| Data Mining | 9 | Bioinformatics | 6 |
| Information Retrieval | 6 | Parallel and distributed systems: paradigms and models | 9 |
| Computational mathematics for learning and data analysis | 9 | Group: BD elective 6 cfu | 6 |
| 33 | 30 |
Second year
Semester 3 |
CFU |
Semester 4 |
CFU |
| Group: free choice | 9 | Thesis | 24 |
| Group: BD elective 9 cfu | 9 | Group: BD elective 9 cfu | 9 |
| Group: BD elective 6 cfu | 6 | ||
| 24 | 33 |
Group: BD electives (9 CFU)
Human languages technologies (AI)
ICT risk assessment (ICT)
Mobile and cyber physical systems (ICT)
Machine learning (AI)
Group: BD electives (6 CFU)
ICT infrastructures (ICT)
Big data analytics (Geospatial Analytics – WDB)
Scientific and large data visualization (CNR)
Peer to peer systems and blockchains (ICT)
Social and ethical issues in computer technology
Computational models for complex systems
3D Geometric Modeling & Processing
Algorithmic Game Theory
Laboratory on ICT Startup Building
Introduction to Quantum Computing
Computational Health Laboratory
For more details on course contents: