Course Content

This semester focuses on the core technical foundations required to become a proficient data-driven AI engineer. It trains students to manage and analyse large, complex, and heterogeneous data at scale, to design distributed data systems, and to apply rigorous machine learning techniques. Students also engage with ethical and societal implications of data and AI and participate in professional seminars presenting the latest developments in the field. The semester has a strong practical component, including projects that integrate concepts across courses. It is composed of the following courses.

• Big Data Management (BDM, 6 ECTS, Prof. Àlex Barceló)
  This course examines the technological and engineering foundations of large-scale data management. It covers distributed and in-memory database systems, stream processing, and DataOps/MLOps practices. Students learn to design scalable architectures capable of handling high data volume, velocity, and complexity.
• Semantic Data Management (SDM, 6 ECTS, Prof. Anna Queralt)
  This course introduces semantic and graph-based data management techniques for integrating and analysing heterogeneous and multimodal data sources. Students learn about RDF and OWL ontologies, graph storage and processing, and semantic data governance for automating data integration in the presence of variety.
• Machine Learning (ML, 6 ECTS, Prof. Marta Arias)
  This course introduces the principles and methods of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. Students learn to analyse, model, and evaluate data-driven solutions using modern machine learning libraries in Python.
• High-Performance Data Analytics (HPDA, 6 ECTS, Prof. Josep-Lluís Berral)
  This course explores computational infrastructures and architectures that enable scalable analytics and AI. It covers parallel and distributed computing, GPU acceleration, and federated learning architectures. Students learn to design and optimise data analytics pipelines for high-performance environments.
• Data Engineering and Artificial Intelligence Seminars (DEAIS, 2 ECTS, Prof. Oscar Romero)
  In this seminar series, students discover recent developments and research in Data Engineering and Artificial Intelligence through lectures from academic and industry experts. They conduct and present a state-of-the-art study on an advanced topic, evaluated jointly during the summer school.
• Debates on Data Ethics (DDE, 2 ECTS, Prof. Petar Jovanovic)
  This course develops students’ critical and communication skills by engaging them in debates on ethical and societal issues arising from data and AI. Students analyse case studies on privacy, bias, and accountability and prepare written reflections on their positions.
• Foreign Language (FL2, 2 ECTS, Dept. of Terminology and Language Services)
  Students take a language course in either Spanish or Catalan, adapted to their proficiency level, to support their academic and cultural integration during the semester at UPC.

Students will attend the summer school organised annually by one partner institution. Presented by leading researchers in the field, it provides students with theoretical and practical skills in the domain. Industrial presentations will allow participants to understand the current product offer.

Although not mandatory, in order to acquire a first working experience, students are encouraged to participate in summer internships, typically with industrial associated partners, between the end of the summer school and the beginning of the third semester.

Specialisations (3rd semester, 30 ECTS)

During the third semester, students choose one of following three specialisation tracks.

Business, Economics and Financial Data Science (UniPD, 3rd semester, 30 ECTS)

In this specialisation, students strengthen their methodological background in data science, focusing on statistical learning, deep learning, and time-series analysis applied to human, business, and financial data. They also explore data-driven business process intelligence and complementary courses on law and data or stochastic methods. The semester combines strong theoretical foundations with applied analytical and modelling skills relevant to modern data-driven decision-making.

• Statistical Learning (StatLearn, 6 ECTS, Prof. Bruno Scarpa)
  In this course, students are introduced to the main concepts and tools of modern statistical inference. They learn to summarise and model data, perform estimation and hypothesis testing, and apply statistical reasoning to real-world problems using R.
• Deep Learning and Human Data Analytics (DeepLearn, 6 ECTS, Prof. Michele Rossi)
  In this course, students learn advanced machine and deep learning techniques, focusing on neural networks and their applications to human-centred and biosignal data. They gain practical experience in designing, training, and evaluating deep learning models.
• Time-Series Analysis for Business, Economic and Financial Data (TimeSeries, 6 ECTS, Prof. Mariangela Guidolin)
  In this course, students study statistical and computational methods for analysing temporal data. They learn to model, forecast, and interpret time-dependent patterns in economic and financial contexts.
• Business Process Intelligence (DataBPI, 6 ECTS, Prof. Massimiliano de Leoni)
  In this course, students learn to model, analyse, and improve business processes using transactional data. The course covers process modelling with Petri nets, process discovery and conformance checking through process mining, and performance evaluation via simulation and what-if analysis.

The students will also choose one between the two following courses:

• Law and Data (LawData, 6 ECTS, Prof. Fiorella Dal Monte, optional)
  In this course, students are introduced to the main legal and ethical issues surrounding data processing and AI. It covers EU data protection law (including the GDPR), data ownership, privacy, and accountability, as well as the emerging legal challenges posed by big data, algorithmic regulation, and artificial intelligence.
• Stochastic Methods (StochMethod, 6 ECTS, Prof. Marco Ferrante, optional)
  In this course, students are introduced to probabilistic and stochastic tools used in data science and network analysis. Topics include Markov chains, Monte Carlo methods, high-dimensional Gaussian models, and random networks, with an emphasis on modelling and computational applications.

In this specialisation, students focus on knowledge-driven data management and artificial intelligence, with depth in graph data management and both symbolic and subsymbolic AI. The semester combines theoretical foundations with practical experience in managing, reasoning over, and extracting knowledge from complex and interconnected data.
Students take the mandatory courses below and complete the semester by choosing 12 ECTS from the elective list.

• Management of Graph Data (GraphData, 6 ECTS, Prof. Katja Hose)
  In this course, students learn to model, query, and manage graph data using both RDF and property-graph paradigms. Topics include graph schemas, querying with SPARQL, Cypher, and PGQL, and ensuring data quality and provenance. Through practical assignments, students gain hands-on experience integrating and analysing graph-based information in real-world settings.
• Graph AI (GraphAI, 6 ECTS, Prof. Emanuel Sallinger)
  This course introduces artificial intelligence methods for graph-structured data, combining graph theory with machine learning. Students study embeddings, graph neural networks, and reasoning over knowledge graphs, applying these methods to link prediction, node classification, and relational pattern discovery in complex data ecosystems.
• Declarative Knowledge and Symbolic AI (SymbAI, 6 ECTS, Profs. Magdalena Ortiz, Mantas Šimkus, Thomas Eiter)
  In this course, students learn logic-based approaches to knowledge representation and reasoning. They explore Datalog, answer-set programming, and description logics for declarative problem solving, and examine how symbolic reasoning complements machine learning in hybrid AI systems.

Elective courses:

• AI Ethics (AIE, 6 ECTS, Kees van Berkel)
  This course addresses the ethical, legal, and social implications of AI systems. Students engage with current debates on fairness, transparency, accountability, and human oversight, and learn to apply ethical reasoning to the design and deployment of intelligent technologies.
• Natural Language Processing and Information Extraction (NLP, 3 ECTS, Prof. Allan Hanbury)
  In this course, students learn computational techniques for analysing and generating natural language. It covers text processing, tagging, neural sequence models, and information extraction, with applications in document understanding and knowledge graph construction.
• Introduction into Research Data Management (RDM, 3 ECTS, Prof. Tomasz Miksa)
  In this course, students learn to manage research data throughout its lifecycle. Topics include FAIR principles, data-management planning, metadata standards, repository systems, licensing, and best practices for open science and reproducibility.
• Technical English Communication (TEC, 3 ECTS)
  In this course, students develop their professional English writing and speaking skills for scientific and technical contexts. They practise creating reports, abstracts, and CVs, participating in interviews and debates, and delivering concise written arguments.
• Technical English Presentation (TEP, 3 ECTS)
  This course focuses on oral communication and presentation skills for technical and academic environments. Students prepare and deliver presentations with structured feedback and are assessed through midterm and final oral evaluations.

In this specialisation, students learn to design and develop intelligent systems capable of processing, reasoning, and learning from heterogeneous and dynamic data sources. The semester combines advanced machine learning, natural language processing, and real-time analytics with research-oriented and human-centred approaches to artificial intelligence.

• Real-Time Data Processing and Analytics (RTProc, 6 ECTS, Prof. Angela Bonifati)
  In this course, students learn techniques for real-time data management and analytics. Topics include stream processing, continuous querying, and modern frameworks such as Apache Flink and Kafka for low-latency, high-throughput data analysis.
• Machine Learning Techniques and Applications (MLTA, 6 ECTS, Prof. Rémy Cazabet)
  In this course, students learn key concepts and methods in modern machine learning, combining theory with hands-on practice in Python. Topics include supervised and unsupervised learning, deep neural networks, graph data mining, and temporal models, with applications explored through exercises and projects.
• Research Methodologies 1 (Research1, 6 ECTS, Prof. Andrea Mauri)
  In this course, students learn the foundations of scientific research and academic writing. It covers how to identify and formulate research questions, review related work, design and conduct experiments, and analyse both quantitative and qualitative data. Students also develop skills in ethical research practices and in presenting and writing scientific papers.
• Natural Language Processing and LLMs (NLPLLM, 3 ECTS, Prof. Andrea Mauri)
  In this course, students learn key concepts in natural language processing and large language models. It covers text processing, transformer architectures, and practical implementation of NLP systems using modern libraries such as spaCy and Hugging Face.
• Research Methodologies 2 (Research2, 3 ECTS, Prof. Andrea Mauri)
  In this course, students learn the fundamentals of research project design and management within the Horizon Europe framework. It covers key aspects of proposal writing, project implementation, and funding instruments such as ERC and Marie Curie grants.
• Human-Centered AI (HumanAI, 3 ECTS, Prof. Andrea Mauri)
  In this course, students learn to integrate human perspectives into the design and deployment of AI systems. It covers topics such as human-AI collaboration, explainability, fairness, and crowdsourcing, with a focus on designing and evaluating human-centered intelligent applications.
• Big Graph Processing Systems (BigGraph, 3 ECTS, Prof. Angela Bonifati)
  In this course, students learn advanced techniques for processing and querying large-scale property graphs. It covers graph data models, query languages such as GQL and SPARQL, and systems for scalable graph analytics. Practical sessions involve querying and transforming real-world graph data using open-source databases like Neo4j.

During the fourth semester, students will put into practice what they have learned during the previous semesters, either in an industrial or a HEI partner. Students are encouraged to devote their master’s thesis to start-up creation. The thesis is evaluated jointly. The thesis work will be considered for submission to scientific conferences.

The closing event of the programme is organised annually by one partner institution. All main partners will participate in the event, associated partners and industrial organisations will be invited to attend. In this event the students will defend their master’s thesis, which will allow all partners to evaluate their skills. The event will also be the ideal place to assess the programme, and to discuss best practices and curriculum evolution. The event will be followed by the graduation ceremony.