As part of the major in Strategic Management of Innovation and Sustainability, the course in Big Data Science and Governance offers an integrated and multidisciplinary approach to data-driven innovation and regulatory frameworks. In the first part of the course, students will acquire advanced quantitative methods for analyzing and extracting value from large-scale datasets and continuous data flows, equipping them with the analytical tools needed to integrate data-driven methods in the decision-making process. Emphasis will be placed on data collection, processing, visualization, and predictive modeling using real-world data sources from both public and private sectors. The second part of the course focuses on the evolving European regulatory and policy framework governing data. Students will engage with key instruments such as the General Data Protection Regulation (GDPR), the Data Governance Act (DGA), the Data Act, the Digital Services Act (DSA), the Digital Markets Act (DMA), the Open Data Directive, and the development of European Data Spaces. The European Union is progressively shaping an internal data market where data are no longer viewed solely through a protectionist or conservative lens — focused on privacy or intellectual property — but are increasingly recognized as a strategic asset, to be leveraged through sharing and reuse mechanisms that enable new opportunities for innovation, value creation, and business growth. Through case-based learning and discussions with experts, the course will explore the legal, ethical, and strategic implications of these frameworks, highlighting both synergies and tensions in their application—particularly in relation to innovation, competition, and sustainability goals. By bridging technical and regulatory perspectives, the course aims to prepare students to navigate and shape the data economy in alignment with European values and global challenges.

Knowledge and comprehension: having demonstrable knowledge and understanding of the subject that builds on and exceeds the level achieved in the first three years; operating at a level where, with the support of specialized literature, there are some aspects that require knowledge of the latest developments in the field. Applied knowledge and comprehension: being able to apply the acquired knowledge and understanding in such a way that shows a professional approach to one’s work, also having competencies for drawing up and deepening arguments, and for solving problems in the field. Making autonomous judgements: being able to collect and interpret relevant information with the aim of building an opinion based on proficiently weighing relevant social, legal, economic and ethical aspects. Communication skills: being able to communicate information, ideas and solutions to an audience consisting of specialists or non-specialists. Ability to learn: having the necessary learning skills to enter a follow-up study that presupposes a high level of autonomy.

First Part (Big Data Science): Big Data: sources and management Data Summarization Introduction to machine learning Natural Language Processing Network Analysis Second Part (Big Data Governance): The value of data in the EU and the evolution of related regulation From personal data protection to data governance The regulation of the data market in the EU From an EU data strategy to a corporate data strategy

First Part (Big Data Science): Slides, lecture notes, and datasets provided by the lecturer. Second Part (Big Data Governance): Handouts, slides, and materials provided directly by the Chair.

Acquisition: lectures, podcasts and online quizzes Practice: guest speakers, case study and simulation Investigation: analyzing ideas and information in a range of materials and resources, using legal databases to collect and analyze data and comparing texts Collaboration: discussing output and building joint output Discussion: seminars, group based class discussion, online forums and synchronous and asynchronous discussion Production: essays, reports and presentations

15% of the final grade is based on attendance and active participation in class (7.5% for attendance and 7.5% for participation through contributions, opinions, initiatives, questions, and comments). In case of justified reasons for non-attendance, these must be communicated and validated by the Chair at the beginning of the course. 35% of the final grade is based on the first written mid-term test (Data Science); 35% of the final grade is based on the second written mid-term test (Data Governance); 15% of the final grade is based on the final oral exam. Non-attending students or students who have failed or refused the grade of one or both mid-term tests may retake them under the same conditions on the day of the oral exam.

Genuine interest in the subject and intellectual appreciation by the Chair.