Presentation

Educational aims

Specific Objectives: The feedback received from stakeholders (employers, students, and faculty), as well as the growing number of academic and popular articles dedicated to the role of the data scientist, confirm the demand for professionals skilled in the production and management of quantitative and qualitative information, and in the valorisation of informational assets within companies and institutions. These professionals are increasingly needed to support management activities and to assess the impact of decision-making processes. In line with the qualifying educational objectives of Class L-41, the Degree Course aims to train graduates with a solid foundation in mathematics, statistics, and data management, capable of working in different application sectors with a good level of autonomy and responsibility. Graduates will be able to enter the job market as qualified professionals, capable of producing and managing information flows, using IT systems to collect and process data, extract strategic insights, and communicate information at the appropriate level of detail, employing the most suitable technological tools, including in big data and statistical learning contexts. Key elements of the Degree Course include: - A strong foundation in computer science, mathematics, and probability and statistics, ensuring that students gain mastery of fundamental scientific methods and principles. - A shared learning pathway for all students, designed to develop competencies across multiple areas of statistical application, with a particular—though not exclusive—focus on economic, demographic, and social themes. A suitable number of credits are allocated to applied statistics and business-related courses, focusing on methodological and applied statistics in economic and social contexts. Students also gain competence in data management, database querying, and the extraction and synthesis of meaningful information using both theoretical and practical techniques from data science. - A wide range of complementary and integrative learning activities. The first group of subjects expands the student’s preparation in the legal aspects of data management, data analytics, and the economic and social application context. The second group deepens methodological and applied statistical knowledge. The third group includes internships, statistical consulting activities, and seminar-based training with a strong interdisciplinary focus, developing soft skills and transferable competencies. In addition, students may choose two free elective courses offered by the University to further strengthen their interdisciplinary and applied learning. To this end, while students are free to choose, the Degree Course will publish an annual list of recommended subjects particularly relevant to the course’s educational goals and to students’ theoretical or applied interests. - A teaching approach that integrates theory and practice. Alongside traditional lectures, students engage in practical exercises and laboratory work, analysing case studies and exploring socio-economic themes with a focus on data literacy and data journalism, and the societal impact of statistical methods and technologies. These activities enhance students’ ability to produce written reports and deliver oral presentations. Many courses adopt innovative teaching methods such as flipped classrooms and continuous use of data analysis software (SAS, R, Python, SQL, etc.), integrated throughout the core curriculum. This approach helps students develop a solid methodological foundation and critical thinking skills, enabling them to pay close attention to data quality and conceptualisation, definition, and measurement issues, as well as to the responsible use of theories and methods in relation to the nature and meaning of available data. - The opportunity to complete an internship in private or public organisations. - The opportunity to participate in statistical consulting activities, simulating real-world consultancy projects with external partners under faculty supervision. This experience aims to provide students with not only technical knowledge but also transversal and professional skills in conducting statistical consultancy. It serves as an important opportunity for professional development and a practical showcase to potential employers, fostering the relational and professional abilities needed to interact effectively with future clients or users.

work perspectives

Profile: Statistical Technician Functions: Graduates in Class L-41 acquire professional skills that combine computer science expertise—particularly in the creation and management of databases using specialised software—with statistical competences in the description, analysis, modelling, and interpretation of data from economic, social, medical/healthcare, and demographic contexts, as well as from all other fields where data analysis is central, including public and official statistics. This professional figure is also capable of developing statistical reports and summaries on the phenomena under examination. Skills: The professional opportunities available to graduates include all areas of work that require competence in data production, processing, management, and interpretation. The data may come from economic, business, social, medical-health, or demographic fields, or any other sector relying on data analysis. The acquired skills correspond to those of an entry-level data scientist. A graduate in Class L-41 may also contribute to the design and evaluation of experiments and controlled clinical trials; conduct quality management and performance measurement assessments in economic, social, or other contexts where data analysis plays a central role; participate in data analysis and processing to investigate various phenomena and make forecasts across multiple application domains; design, manage, and use databases for different purposes, including big data and statistical learning environments. Career Opportunities: Graduates in Class L-41 can work as statistical technicians in public administrations, planning and research offices, companies operating in biomedical, epidemiological, economic, and social sectors, statistical departments of small and large enterprises, marketing departments, information systems management companies, statistical consultancy firms providing external support to private and public organisations, public and private research institutions, organisations producing and disseminating official statistics. The skills and knowledge acquired during the Degree Course also allow for further study in Master’s programmes in Statistics and Data Science (LM-82, LM-83, and LMDATA), and—upon obtaining additional credits—in other related areas such as economics.

Characteristics of the final exam

To obtain the degree, the student must have earned 180 university credits, including those awarded for the final exam. The final exam aims to assess the graduate’s level of maturity and critical thinking in relation to the knowledge and skills acquired throughout the programme, as defined by the academic regulations. The final exam consists of the preparation—and, if applicable, the discussion—of a written and/or multimedia dissertation. The work may be based on the topic of an internship or on a subject chosen from those proposed by the Joint Degree Course Council, according to the procedures established by the Degree Course regulations, in compliance with ministerial requirements and the University’s specific guidelines.