This website uses cookies

The website of the Fundació Politècnica de Catalunya uses its own and third-party cookies to improve the browsing experience and for statistical purposes. For more information about cookies you can consult the cookie policy.

Reject all cookies
Manage cookies
Allow all cookies
Home   >  Master's & postgraduate courses  >  Education  >  Continuing education master's degree in Big Data, Data Science & Engineering
Information session

08-09-2026

Online
We advise you! Request information or admission
  • discount

    5% discount on tuition until September 10

Presentation

Edition
10th
Credits
60 ECTS (324 teaching hours)
Delivery method
Face-to-face
Language of instruction
Spanish
Fee
€9,300 €8,835(5% discount on tuition until September 10)
Special conditions on payment of enrolment fee and 0,7% campaign

Finance your registration with Sabadell Consumer . Use the simulator to easily calculate your fees and request it through your Program Advisor.

Registration open until the beginning of the programme or until end of vacancies.
Dates
Classes start: 05/10/2026
Classes end: 17/07/2027
Programme ends : 22/11/2027
Class schedule
Monday: 6:00 pm to 9:00 pm
Wednesday: 6:00 pm to 9:00 pm
Friday: 6:00 pm to 9:00 pm
Taught at
Facultat d'Informàtica de Barcelona (FIB)
C/ Jordi Girona, 1-3
Barcelona
Why this programme?
Data is now an essential asset for any organisation, and professionals specialising in Big Data are among those most in demand and with the strongest future prospects. According to the Digital Talent Overview 2025 by Mobile World Capital Barcelona, these profiles are expected to experience growth of up to 110% between 2025 and 2030. At the same time, artificial intelligence and Big Data are among the skills expected to become increasingly important in the coming years. In this context, the digitalisation of products, services and processes is generating growing volumes of information from highly diverse sources: corporate systems, applications, sensors, social media, documents, images, audio and graphs. But having data is not enough: organisations need professionals who can collect, integrate, store and process data, and turn it into knowledge to support better decision-making and create value.

The Master's Degree in Data Science & Engineering covers this entire journey, from building robust, scalable and well-governed data ecosystems to advanced analytics and knowledge extraction. Students develop expertise in Data Engineering and Data Analytics, working with cloud architectures, distributed systems, batch and real-time processing, NoSQL, document, columnar, graph and vector databases, as well as DataOps and MLOps best practices. Building on this foundation, the programme covers Data Mining and Machine Learning methods, techniques for analysing time series and graph-structured data, and the processing of complex and unstructured data such as text, images and audio. Embeddings make it possible to represent this data in vector spaces and connect it with advanced artificial intelligence techniques, including transformer architectures, which underpin large language models and many multimodal systems, as well as graph neural networks.

The programme's key differentiator is its combination of strong mathematical and technical foundations, knowledge of current technologies and a highly practical approach. The aim is not simply to learn how to use specific tools, but to understand the principles behind the methods and develop the ability to adapt to the evolution of the field. Learning is based on real-world cases, hands-on activities and a final project that brings together the different stages of a data-driven system. In this way, the programme prepares professionals to design complex data systems, apply advanced analytical techniques and assess their feasibility and impact from a business perspective.
Aims
  • Understand and manage the complete data lifecycle, from data collection and integration to analytical exploitation and value creation.
  • Design robust, scalable and well-governed data ecosystems, identifying the functional, performance, scalability, availability and consistency requirements that should guide the selection of the appropriate data architecture.
  • Work with cloud architectures, distributed systems and batch and real-time processing environments, applying DataOps and MLOps best practices to develop, deploy, monitor and maintain data-driven systems.
  • Select and use the most appropriate storage models for each type of data, including NoSQL, document, columnar, graph and vector databases.
  • Prepare, transform and integrate structured, semi-structured and unstructured data, including text, images, audio and graph-structured data, to enable their analysis.
  • Formalise real-world problems as Data Analytics problems and select and apply the most appropriate Data Mining and Machine Learning methods, including specific techniques for analysing time series, relational data and graph-structured data.
  • Represent and analyse complex data using advanced Artificial Intelligence techniques, including embeddings, deep learning, transformers, large language models (LLMs), graph neural networks and multimodal AI, to integrate and jointly analyse textual, visual, audio and structured information.
  • Evaluate the quality, predictive capability, robustness and limitations of analytical models, communicate and visualise results in an accessible and useful way, and assess the feasibility, impact and value creation potential of data-driven projects, incorporating principles of governance, privacy, responsibility, ethics and regulatory compliance.
Who is it for?
  • Computer Engineers or equivalent interested in retraining in the field of Big Data.
  • Information technology professionals, developers, architects, data analyst and systems administrators, interested in retraining in the field of Big Data.
  • Bachelor's degree in Engineering, in Mathematics or Stadistics. In these cases, the people who apply for admission must have technical training in centralized databases and programming.

The programme is focused on creating mixed profiles in Data Analytics and Data Management.

Training Content

List of subjects
12 ECTS 72h
Data Management
  • The Data-Driven Society and the Data-Driven Paradigm
    • The value of data in organisations
    • Use cases and data-intensive systems
    • Cloud computing and service engineering (XaaS)
    • Requirements of modern data systems
    • Limitations of traditional systems and the emergence of new data management paradigms
  • Fundamentals of Distributed Data Systems
    • Data structures and optimisation principles
    • Data partitioning, replication and distribution
    • Scalability, availability and consistency
    • Distributed and parallel processing
    • In-memory data management and processing
  • Data Lifecycle, Integration and Processing
    • Ingestion and integration of data from heterogeneous sources
    • ETL and ELT processes
    • Batch and real-time processing
    • Event-driven architectures and data streams
    • Distributed processing
    • Stream processing (real-time)
    • Data pipeline orchestration and automation
  • Analytical Storage Architectures
    • Operational databases and analytical systems
    • Data warehouses, data lakes and lakehouse architectures
    • Distributed storage and object storage
    • Columnar representations and formats
    • Parquet and other formats for efficient data exchange and processing
    • Compression, partitioning and block processing
  • NoSQL Data Models and Distributed Storage
    • Principles and characteristics of NoSQL systems
    • Key-value models and wide-column databases
    • Distributed storage
    • Physical data modelling considerations
    • Hadoop and MapReduce as foundations of large-scale distributed processing
    • Criteria for selecting the most appropriate data model and storage system
  • Document Databases and Search Systems
    • Concepts and principles of document-oriented databases
    • Modelling and management of semi-structured data
    • Queries and processing using the Aggregation Framework
    • Indexing, text search and distributed analytics with Elasticsearch
    • Lexical search, inverted indexes and information retrieval
  • Graph Data Management and Processing
    • Graph concepts, models and types
    • Property graph modelling
    • Graph management and querying
    • Graph algorithms and distributed graph processing
    • Semantic graphs and knowledge representation
    • Graph models and languages: RDF, RDFS and SPARQL
  • Vector Representations and Vector Databases
    • Concepts and characteristics of embeddings
    • Generation, storage and management of embeddings
    • Vector indexing and approximate nearest-neighbour search
    • Similarity search, metadata filtering and hybrid search
    • Management of text, image, audio and graph embeddings
    • Integration of vector databases into semantic search systems, RAG and multimodal applications
  • Modern Data Architectures
    • Reference architectures for data-intensive systems
    • Batch, streaming and event-driven architectures
    • Cloud-native architectures and service-based systems
    • Integration of operational, analytical, graph and vector databases
    • Architectures for deploying Data Science and Artificial Intelligence solutions
    • Technology selection criteria and architectural design
  • Data Governance, DataOps and Data System Operations
    • Data quality, validation and data contracts
    • Metadata, data catalogues and data lineage
    • Security, privacy and access control
    • Pipeline orchestration, monitoring and observability
    • Version control, testing and deployment automation
    • DataOps principles and best practices
    • Integration with MLOps and lifecycle management of LLM-based models and applications
12 ECTS 72h
Data Analytics
  • Introduction
    • What is knowledge discovery?
    • Basic statistics.
    • Introduction to R.
  • Pre-processing of data
    • Data cleansing and adjustment.
    • Transformations.
  • Basic analysis techniques
    • Multiple regression.
    • Profiling.
  • Multivariate analysis
    • Principal component analysis.
    • Clustering.
    • Decision trees.
  • Machine learning
    • Concept.
    • Mathematical foundations.
  • Main machine learning techniques
    • Association rules.
    • Supervised linear methods.
    • Neuronal networks.
    • Support vector machines.
    • Random forests.
  • Text processing
    • Pre-processing and preparation of data.
    • Main text mining techniques.
    • Information retrieval.
  • Time series analysis
    • Pre-processing and preparation of data.
    • Forecasting
    • Identifying outliers.
  • Advanced data analysis
    • R packages for parallel processing.
    • R and relational databases.
    • Data analysis in distributed environments using HDFS and Spark
      • Spark R and MLlib.
16 ECTS 96h
Hands-on Experience: Data Management and Analytics
  • Infrastructure
    • Introduction to Cloud environments.
    • Virtualization.
    • Oracle services.
  • Distributed storage
    • The Hadoop Ecosystem.
    • Key-Value Systems: HBase.
  • Distributed processing
    • MapReduce.
    • Spark: SparkSQL. Spark Streaming. Spark Graphs.
    • Data analysis in distributed environments.: MLlib. SparkR.
  • Document Stores
    • MongoDB.
    • Elasticsearch.
  • Graph databases
    • Property-graphs: Neo4J.
    • Semantic graphs: GraphDB.
  • Distributed data analysis
    • Data analysis in distributed environments using Hadoop Distributed File System (HDFS) and Spark.
      • SparkR and MLlib.
  • Big data systems architecture
5 ECTS 33h
Business and Entrepreneurship in Big Data
  • Introduction: The competitive environment of the company and big data
    • Big data landscape.
  • Marketing
  • Business ideation techniques
  • Clients and users.
  • Definition of products and services.
  • Business modelling tools: Business model canvas
    • Constituent parts.
    • Practical cases.
    • Resolution of cases: Twitter, Facebook, etc.
  • Budget and financing process
    • Finance.
    • Private funding: Business Angels and Venture Capital.
    • Public funding.
  • Presentations and pitch sessions
  • Creating a business
  • Legal issues: Data regulation.
  • Financial considerations.
  • Ethical considerations of Big Data: Business and Privacy
  • Elevator Pitch and presentations
15 ECTS 51h
Project
  • Visualization
    • Visualization processes.
    • Visualization techniques.
  • Project management

Project will be carried out in groups of 2 or 3 people, that will have to develop a real case applied. The students will have to perform an analysis and give an innovative solution to the problem prosed. In the development of the project, the quality of the technical solution developed and the added value it brings to the business are valued.

The groups have to define the roles of each component and use agile methodologies.
The UPC School reserves the right to modify the contents of the programme, which may vary in order to better accommodate the programme objectives.
Degree
University-specific Master's Degree in Continuing Education in Data Science and Engineering, Issued by the Universitat Politècnica de Catalunya. Issued in accordance with the provisions of Article 7.1 of Organic Law 2/2023, of 22 March, on the University System; Article 36 of Royal Decree 822/2021, of 28 September, which establishes the organisation of university education and the procedure for quality assurance; and Articles 2 and 8 of the Amendment to the Regulations for Continuing Education Programmes, approved by Agreement CG/2025/02/35, of 25 March, of the Governing Council of the UPC. To obtain this qualification, it is necessary to hold a prior university degree equivalent to level 2 of the Spanish Qualifications Framework for Higher Education (MECES). Otherwise, the student will receive a certificate of completion of the programme issued by the Fundació Politècnica de Catalunya. (See details appearing on the certificate).

Learning methodology

The teaching methodology of the programme facilitates the student's learning and the achievement of the necessary competences.



Learning tools
Participatory lectures
A presentation of the conceptual foundations of the content to be taught, promoting interaction with the students to guide them in their learning of the different contents and the development of the established competences.
Practical classroom sessions
Knowledge is applied to a real or hypothetical environment, where specific aspects are identified and worked on to facilitate understanding, with the support from teaching staff.
Solving exercises
Solutions are worked on by practising routines, applying formulas and algorithms, and procedures are followed for transforming the available information and interpreting the results.
Success stories
Outstanding business knowledge and experiences with high added value acquired during an outstanding professional career are presented and shared.
Problem-based learning (PBL)
An active learning methodology that enables the student to be involved from the beginning, and to acquire knowledge and skills by considering and resolving complex problems and situations.
Tutorship
Students are given technical support in the preparation of the final project, according to their specialisation and the subject matter of the project.
Assessment criteria
Attendance
At least 80% attendance of teaching hours is required.
Solving exercises, questionnaires or exams
Individual tests aimed at assessing the degree of learning and the acquisition of competences.
Completion and presentation of the final project
Individual or group projects in which the contents taught in the programme are applied. The project can be based on real cases and include the identification of a problem, the design of the solution, its implementation or a business plan. The project will be presented and defended in public.
Work placements & employment service
Students can access job offers in their field of specialisation on the My_Tech_Space virtual campus. Applications made from this site will be treated confidentially. Hundreds of offers of the UPC School of Professional & Executive Development employment service appear annually. The offers range from formal contracts to work placement agreements.
Virtual campus
The students on this continuing education master's degree will have access to the My_ Tech_Space virtual campus - an effective platform for work and communication between the programme's students, lecturers, directors and coordinators. My_Tech_Space provides the documentation for each training session before it starts, and enables students to work as a team, consult lecturers, check notes, etc.

Teaching team

Academic management
  • Jovanovic, Petar
    Jovanovic, Petar
    info
    View profile in futur.upc
    PhD in Computer Science from the Polythecnic University of Catalonia (UPC) and Université Libre de Bruxelles. MSc in Computer Science from the UPC. BSc in Software Engineering from University of Belgrade. His research is in the area of Business Intelligence, big data Management systems and distributed databases.
  • Romero Moral, Òscar
    Romero Moral, Òscar
    info
    View profile in futur.upc
    Doctor in Informatics from the UPC. Lecturer in the Department of Service and Information System Engineering at the UPC. Teaching at both undergraduate and official master's degree level. UPC coordinator of the Erasmus Mundus Master's Degree in Big Data Management and Analytics (BDMA) and the master's degree in Data Science from the UPC. Researcher in the field of data and information management, in which he has published more than sixty publications in conferences and international journals. He has worked as a consultant with SAP, HP and the WHO, among others.
Teaching staff
  • Aluja Banet, Tomàs
    info
    View profile in futur.upc
    Professor at the Polytechnic University of Catalonia (UPC). He is the author of 60 articles published in scientific journals or as chapters of a book. Research topics addressed: Multivariate analysis, data mining models, models for estimating intangibles and design of learning analytics systems. Member of scientific committees of international conferences (including Computational Statistics, COMPSTAT, and PLS). He has participated in various European and Spanish research projects in the field of systems based on statistical meta-data, data fusion and modeling of intangibles, and has been a statistical consultant for La Caixa, Kantar Media, Idescat and the City Council of Barcelona among others.
  • Barcelo Cuerda, Alex
    info
    View profile in futur.upc / View profile in Linkedin / View profile in Orcid / View profile in Google Scholar
    PhD in Computer Architecture from the Universitat Politècnica de Catalunya (UPC). After simultaneously pursuing a degree in Mathematics, Telecommunications Engineering, and Electronic Engineering, he began working at the Barcelona Supercomputing Center, where he developed his PhD on topics related to distributed storage and high-performance computing (HPC). At this research center, he works as a researcher and participates in numerous European projects. He is currently a lecturer in the ESSI department at the Barcelona School of Informatics (FIB, UPC).
  • Belanche Muñoz, Luis Antonio
    info
    View profile in futur.upc
    Graduated in Computer Science and holds a doctorate in Artificial Intelligence from the Universitat Politècnica de Catalunya (UPC). He is a professor in the Computer Science Department of the UPC with more than thirty years of teaching experience. He has supervised or tutored more than one hundred theses and student projects. He currently teaches on the bachelor’s degree in Data Science and Engineering, the master's degree in Innovation and Research in Informatics (MIRI), the master's degree in Advanced Mathematics and Mathematical Engineering (MAMME), the master's degree in Artificial Intelligence (AI) and the master's degree in Data Science at the Barcelona School of Informatics (FIB). He has authored more than one hundred and thirty publications in international journals and conferences, and has participated in fifteen research projects. He was recently head of studies at the Barcelona School of Informatics (FIB).
  • Berbegal Castelló, José
    info
    View profile in Linkedin
    Computer Engineer from the Universitat Politècnica de Catalunya (UPC). He has nearly 20 years of experience in the field of defense and security. He is currently the head of the software and artificial intelligence department at aunav, the robotics division of the Escribano Mechanical & Engineering group, where he leads a multidisciplinary team dedicated to the development of advanced software and intelligent autonomy capabilities for highly complex robotic systems.
  • Berral García, Josep Lluís
    info
    View profile in futur.upc / View profile in Linkedin / View profile in Orcid / View profile in Google Scholar
    A computer engineer with a master's degree in Computer Architecture and doctorate in Computer Science from the Universitat Politècnica de Catalunya (UPC). His research focuses on management of computational resources in cloud systems using data mining and machine learning. He is currently a researcher in the Computer Architecture Department at the UPC and at the Barcelona Supercomputing Center (BSC), and leader of the CROMAI research group. He is a specialist in data centre management (cloud computing), machine learning, data mining and analysis and artificial intelligence.
  • Bilalli, Besim
    info
    View profile in futur.upc / View profile in Linkedin
    PhD in Computer Science jointly from Universitat Politècnica de Catalunya (UPC) and Poznan University of Technology. A postdoctoral fellow and teaching assistant at UPC as part of the Database Technologies and Information Management group. He is involved in research and teaching activities that span from data management to machine learning. His research interests lie in the areas of data management, data pre-processing, and on applying machine (meta) learning techniques on providing user support for the different data analytics steps. A programme committee member for the Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP) and DaWaK conferences.
  • Deulofeu Aymar, Joaquim
    info
    View profile in futur.upc / View profile in Linkedin

    PhD in Economic and Business Sciences from the University of Barcelona (UB). International EFQM assessor-advisor, founder and CEO of Qualitat, Serveis Empresarials, S.L., a company where he has carried out his professional activity as a consultant for 31 years. Associate Professor in the Department of Business Organization at the Polytechnic University of Catalonia (UPC) for 34 years, President of the Baix Montseny Economic and Social Circle for 15 years, and Secretary of the Board of Trustees of the Sant Celoni Hospital Foundation for 18 years.

  • Díaz Iriberri, José
    info

    Holds a doctorate in Computing from the Universitat Politècnica de Catalunya (UPC). He currently specialises in research in the field of computer graphics, is a senior lecturer at the University of Vic, and works as a lecturer on various bachelor's degree programmes and postgraduate courses at the UPC. He has been a post-doctoral researcher on the European Commission Marie Curie Fellowship Programme in the CRS4 Visual Computing group (Italy). His research focuses on the visualisation of scientific data, parallel computing and GPU programming, and the design of applications in virtual and augmented reality environments.
  • Escolano Peinado, Carlos
    info
    View profile in futur.upc / View profile in Linkedin
    Doctor in Computer Science from the Universitat Politècnica de Catalunya (UPC) and a master's degree in Artificial Intelligence from the UPC. He is currently a researcher in the language technologies group at the Barcelona Supercomputing Center (BSC), as well as an associate professor in the Department of Computer Science at the UPC. His area of expertise is natural language processing, especially multilingual machine translation with neural networks.
  • Galí Reniu, Ferran
    info
    View profile in Linkedin
    Is passionate about web scale distributed systems. While working on Big Data technologies for several years, he gained expertise solving problems that require a massive amount of data processing. Starting with the Hadoop ecosystem ten years ago, he has tried to keep up to date with the state of the art either by making an impact at work, collaborating with education institutions, or getting involved in the community. He is currently working at LifullConnect on products that involve real-time data processing, while embracing Kafka, Avro, Kotlin and ElasticSearch.
  • González Alonso, Pedro Javier
    info
    View profile in Linkedin
    Computer Engineer, with a Master's in Innovation and Research in Informatics (specializing in Data Science) from the Universitat Politècnica de Catalunya (UPC), an MBA from Esade, and a postgraduate degree in Artificial Intelligence from UPC School. Over the past 12 years, he has held positions as Chief Technology Officer (CTO), Head of Data Science (HDS), and Chief Data Officer (CDO) at tech startups focused on AI and data science.
  • Gutiérrez Torre, Alberto
    info
    View profile in Linkedin View profile in Orcid View profile in Google Scholar
    A doctor in Computer Architecture and the holder of a master's degree in Innovation and Research in Computing, with a mention in Data Science from the Universitat Politècnica de Catalunya (UPC). He is currently a post-doctoral researcher in the Data-Centric Computing group at the Barcelona Supercomputing Center (BSC) and the principal investigator of the European INCISIVE, CALLISTO and SECURED projects, researching application optimisation with high performance computing and distributed medical data analytics with federated learning.
  • Hmimou, Achraf
    info
    View profile in futur.upc / View profile in Linkedin / View profile in Orcid / View profile in Google Scholar
    Data Science research assistant who holds a Master's degree in Data Science (2022-2024) and a Bachelor's degree in Computer Science (2018-2022). During the academic journey, he enriched his perspective through international experiences at Polytechnique Montreal and Beijing Institute of Technology. Professional experience includes internships at CIEVilanova and Adevinta Spain, providing practical industry exposure. Main research interests focus on Data Governance, Knowledge Graph applications, and Distributed Systems, aiming to contribute to the advancement of data management and infrastructure.
  • Jovanovic, Petar
    info
    View profile in futur.upc
    PhD in Computer Science from the Polythecnic University of Catalonia (UPC) and Université Libre de Bruxelles. MSc in Computer Science from the UPC. BSc in Software Engineering from University of Belgrade. His research is in the area of Business Intelligence, big data Management systems and distributed databases.
  • Martínez Fernández, Silverio Juan
    info
    View profile in futur.upc / View profile in Linkedin / View profile in Orcid / View profile in Google Scholar
    Associate Professor at UPC-BarcelonaTech since 2024. PhD in Computer Science. Former Beatriz Galindo Junior Distinguished Researcher (2020–2024) and Post-Doctoral Fellow of the European Research Consortium for Informatics and Mathematics (2016–2018) at Fraunhofer IESE (Germany).

    Principal investigator in research and technology transfer projects focused on the environmental sustainability of ML-based software. Has served as Program Committee co-chair at various international conferences and workshops.

  • Maynou Yelamos, Marc
    info

    Computer Engineer from the Universitat Politècnica de Catalunya (UPC). Master's degree in Data Science from the UPC. He is currently a PhD student and associate professor at the UPC as part of the Database Technologies and Information Management (DTIM) research group. He teaches in undergraduate (Degree in Computer Engineering) and master's degree (Master in Data Science). His research focuses on the areas of data management and data discovery, both in the context of Big Data.
  • Montornés Solé, Jordi
    info
    View profile in Linkedin
    Computer Engineer by the Universitat Politècnica de Catalunya. Since 2004, hHe has worked at companies like Caixa Catalunya, HP and Vueling. Currently, he works as team leader at Ocado Technology
  • Nadal Francesch, Sergi
    info
    View profile in futur.upc / View profile in Linkedin
    The holder of a doctoral degree in computer science from the Universitat Politècnica de Catalunya (UPC) and the Université Libre de Bruxelles (ULB). He is currently a lecturer in the Department of Service and Information System Engineering at the UPC, where he teaches in the Faculty of Computer Science on the bachelor's degree in Computer Engineering, the bachelor's degree in Artificial Intelligence and the master's degree in Data Science. His research interests are in the field of data and information management, as well as data lifecycle automation, an area in which he has published numerous papers and led technology transfer projects.
  • Pons Recasens, Gerard
    info

    Graduated in Physical Engineering from the Universitat Politècnica de Catalunya (UPC) and Master in Data Science from the UPC. He is currently a PhD student in Computing at the UPC and is part of the Database Technologies and Information Management (DTIM) research group.
  • Queralt Calafat, Anna
    info
    View profile in futur.upc
    Holds a doctorate in Computer Science from the Universitat Politècnica de Catalunya (UPC). She is currently a lecturer in the Department of Service and Information System Engineering at the UPC, and head of the Database Technologies and Information Management research group. She also works with the Barcelona Supercomputing Center as head of the Distributed Object Management research line, researching distributed data management in high-performance and edge-to-cloud environments.
  • Romero Moral, Òscar
    info
    View profile in futur.upc
    Doctor in Informatics from the UPC. Lecturer in the Department of Service and Information System Engineering at the UPC. Teaching at both undergraduate and official master's degree level. UPC coordinator of the Erasmus Mundus Master's Degree in Big Data Management and Analytics (BDMA) and the master's degree in Data Science from the UPC. Researcher in the field of data and information management, in which he has published more than sixty publications in conferences and international journals. He has worked as a consultant with SAP, HP and the WHO, among others.
  • Segarra Alonso, Roger
    info
    View profile in Linkedin
    Graduate and Master's Degree in Law from ESADE Business Law School in 2008. Member of the Barcelona Bar Association. He is currently a partner at the international law firm Osborne Clarke. He has more than 15 years of experience in new technologies, data protection and intellectual property law in the health and life sciences, digital and retail sectors.
  • Soler Gomis, Lluis
    info
    View profile in Linkedin
    Bachelor’s degree in Business Administration and Management from the Abat Oliba University, MBA from Instituto de Empresa, and a Master’s degree in eCommerce from La Salle. Entrepreneur and business owner. He is currently CEO and founder of SoftDoit, HelloSoftware, and El Club del Software, among others.
    He is also the author of the book Pyme Minimalista: how to earn more and live better.
  • Torrent Moreno, Marc
    info
    View profile in Linkedin
    Holds a degree in Telecommunications Engineering from the UPC, a PhD in Computer Science from the University of Karlsruhe and an Executive MBA from Esade. He has more than twenty years of experience in R&D+i in the field of ICT, and has been part of various organisations in Europe and the USA (British Telecom, NEC Deutschland, Mercedes-Benz USA, UC Berkeley, Ficosa Internacional and Eurecat). He focuses on the world of data an AI, and has led the creation of the Big Data CoE and the Centre of Innovation for Data Tech and AI in Catalonia. He is currently head of Data & Analytics for the BonPreu group, and a lecturer at several Catalan universities.
  • Torrents Poblador, Pere
    info
    View profile in Linkedin
    Economist, he has developed his professional career in the areas of business development and business management in the video games' industry. Currently, he is Director of Operations at AnchorPoint Studios - at NetEase Games Studio and professor at Universitat Politècnica de Catalunya, School of Professional Executive Development and Centre de la Imatge i Tecnologia Multimèdia in business and finance subjects, in Degrees and Masters in Video Games and Master in Big Data Management.
  • Touma, Rizkallah
    info
    View profile in Linkedin
    PhD in Computer Science from the UPC. MSc in Business Intelligence from the UPC and the Université Libre de Bruxelles. He currently leads the big data and data spaces research line at the i2CAT Foundation. He has worked as a junior researcher with the Barcelona Supercomputing Center and has participated in several pan-European projects (BigStorage, SoCaTel, CORDISBOOST 4.0, IoT-NGIN, TEADAL, CODECO).
  • Vázquez Alcocer, Pere-Pau
    info
    View profile in futur.upc
    PhD in software from the Universitat Politècnica de Catalunya (UPC). Professor at the UPC, he, currently, teaches undergraduate and master courses at UPC. He has previous experience teaching undergraduate and master courses in other universities such as the University of Nuremberg, the University of Girona, the Universitat Oberta de Catalunya, or the University of Vic. His research area focuses on scientific data visualization and computer graphics.

Associates entities

Strategic partners
  • Facultat d'Informàtica de Barcelona. FIB (UPC)
    • Disseminates the programme in the professional sphere and area of expertise.
Collaborating partners

Career opportunities

  • Data Scientist.
  • Digital Transformation Leader.
  • Data Engineer.
  • Chief Data Officer.
  • Data Architect.
  • Big Data Consultant.
  • Data Analyst Consultant.
  • Decisional Systems Engineer.

Request information or admission

Information and guidance:
Olga Garrán González
(34) 93 112 08 67
Request received!
Your request has been received successfully.

The University will be closed from August 1 to 31 (both inclusive). During this period we will not be able to attend to queries or carry out administrative procedures.

From September 1, we will resume normal activity and respond to your message as soon as possible.

Thank you for your understanding and for your interest in our training programs.
Error
Due to an error in the connection to the database, your submission has not been processed. Please try again later, phone us on (34) 93 112 08 08, or send us an email at: webmaster.fpc@fpc.upc.edu
You have exceeded the maximum size of the file
Your details are not registered in the staff database.
You can register by filling out the form.
Please check your payment details. The platform has returned an error.
You can make the payment again without re-entering the data.

Name:

Programme: Big Data, Data Science & Engineering

Fee: €9,300 €8,835(5% discount on tuition until September 10)

Submit and make the payment
  • If you have any doubts.
  • If you want to start the registration procedure.
How to start admission
To start the enrolment process for this programme you must complete and send the form that you will find at the bottom of these lines.

Next you will receive a welcome email detailing the three steps necessary to formalize the enrolment procedure:

1. Complete and confirm your personal details.

2. Attach any additional required documentation, whenever this is necessary for admission.

In addition to your CV, the UPC School will also require you to submit the following documents for preregistration on this Continuing education master's degree:
    • Letter of motivation, describing your background (training and experience) and the reason for completing the program.

3. Pay €110 in concept of the registration fee for the programme. This fee will be discounted from the total enrolment fee and will only be returned when a student isn't admitted on a programme.

Once the fee has been paid and we have all your documentation, we will assess your candidacy and, if you are admitted on the course, we will send you a letter of acceptance. This document will provide you with all the necessary information to formalize the enrolment process for the programme.





  date protection policy

* Mandatory fields

Basic information or first layer on data protection

Controller

Fundació Politècnica de Catalunya (FPC). + INFORMATION

Purpose of processing

Respond to requests for information from the data subjects on training activities managed or carried out by the FPC. + INFORMATION

Establishment or maintenance of the academic relationship with the data subject. + INFORMATION

Send information on the activities of the FPC. + INFORMATION

Legitimation

Data subject's consent. + INFORMATION

Legitimate interest in the development of the academic relationship. + INFORMATION

Addressees

No assignments or communications.

Rights

Access, rectification, erasure, restriction, object and data portability. + INFORMATION

Data protection officer contact details

info.dpo@fpc.upc.edu

Additional information

Privacy policy of our website. + INFORMATION

Storage limitation

Privacy policy of our website. + INFORMATION

Payment services

In the event that the data subject enters into a formal relationship with the FPC, the data subject authorises and consents to the charge, thereby expressly waiving the right to a refund of the charge.

ENROLMENT CONDITIONS OF THE FUNDACIÓ POLITÈCNICA DE CATALUNYA

Introduction

The Fundació Politècnica de Catalunya (FPC), with tax identification number G60664000, registered in the Register of Foundations of the Government of Catalonia under no. 834, designs, promotes and manages the continuing education programmes of the Universitat Politècnica de Catalunya (UPC), as well as other training activities it promotes. The academic regulation of the UPC's continuing education programmes is set out in Agreement CG/2025/02/35, of 25 March, of the Consell de Govern, which approves the update of the amendment to the regulations governing continuing education programmes. And Articles 36 and 37 of Royal Decree 822/2021, of 28 September, which establishes the organisation of university education and the procedure for ensuring its quality. The UPC Social Council approves the price of continuing education programmes, as well as discounts, grants and ancillary services for each academic year.


Provision, knowledge and acceptance of the Enrolment Conditions

Reading and accepting these Enrolment Conditions is an essential requirement for studying at the FPC, as they constitute the basis of the training services contract that participants sign electronically with the FPC. These Enrolment Conditions are available to users on the Transparency Portal: https://www.fpc.upc.edu/cat/fpctransparent/normativa/. These Conditions are applicable to all courses taught by the FPC, with the exception of those aimed at other university institutions and public and private entities, which shall be governed by the legal instruments binding on the participating institutions.


Admission and registration fees

The admission process may require the advance payment of registration fees; this amount will be deducted from the tuition fee once admitted and will only be refunded in the event of non-admission, deferral or non-completion of the course, in the latter case, only if the letter of admission is valid. The admission process concludes with the sending of the admission letter to the participant, which includes the details of the course, the period of study, the tuition fees and the payment deadlines.


Enrolment

The enrolment process is formalised with the first payment of the course fee, whether partial or total. Therefore, this first payment of the course fee corresponds to the signing of the contract for the provision of training services regulated in these Conditions, regardless of whether the total price or any of the agreed payment instalments have been paid in full.

It is the participant's responsibility to find out about discounts on the enrolment fee and to prove that they meet the relevant criteria to benefit from them, always prior to formalising their enrolment; otherwise, they will not be able to benefit from them. Discounts and grants cannot be combined, unless they are not incompatible and this is expressly stated.

The participant declares that they are aware of and accept the calls for applications and the terms and conditions of the financial aid corresponding to the current academic year for which they have applied to be a beneficiary. If the student does not complete or withdraws from the course for which they have received aid, a discount or a grant within the period specified in the corresponding call for applications or in the enrolment document, they must return the amount granted or deducted from the price to the FPC. The provisions of the section on Changes to enrolment in these conditions shall not apply to this participant.

Enrolment is personal and non-transferable, so that once formalised, it shall only entitle the natural person who has been identified as a candidate and, subsequently, as an admitted person to undertake the course of study.

The enrolment fee may be paid in full or in part by third parties, although the right/obligation to follow the training course corresponds to the participant, without the paying party being able to interfere with or prevent the exercise of this right in any way. The foregoing is without prejudice to the FPC's right to prevent the participant from continuing in the cases provided for in the section on Non-payment of the enrolment fee provided for in these Conditions.

The amount paid for enrolment will not be refunded once the study has begun, nor after 14 calendar days from the date of payment. Otherwise, the right of withdrawal may be exercised. The amount paid will only be refunded if the study is postponed or does not take place.

Notwithstanding the above, and on an exceptional basis, a tuition fee refund will be issued if the following circumstances apply:

  • Visa refusal
    • For courses starting in-person: proof must be provided in the form of the refusal letter, and the refund must be requested within a maximum of 15 days following the start of the course; the refund will be in full.
    • For courses starting online and ending in person: proof must be provided in the form of the refusal letter, always before the in-person start of the course, and the refund will be proportional to the credits not taken.
  • Serious illness or accident affecting the participant: proof must be provided in the form of an official medical certificate, stating the start date of the illness and the expected period of convalescence. If this occurs before the start of the course, a full refund will be issued. If this occurs after the start of the course, the refund will be proportional to the credits not taken.

In both cases only, the FPC will refund, as appropriate, the total or proportional amount paid by the participant, minus €300 to cover the costs of processing the academic record.


Subsidised training

The FPC is not responsible for fulfilling the academic and/or administrative requirements for the contracted training to be subsidised. The participant or the person paying for the training does so at their own risk and exempts the FPC from any liability or compensation.


Change of enrolment

Requests to change enrolment, whether in terms of study or teaching method, must be made within 15 calendar days of the start date of the original course. Requests made after this period will not be accepted. The request will be assessed and its suitability determined on a case-by-case basis. When the change involves an increase in the total enrolment fee, the participant will be responsible for the difference. When the change involves a decrease in the enrolment fee, the difference will be refunded. Once evaluated, and regardless of the outcome of the request, changes to enrolment will incur a cost of €300 for the applicant for the processing of academic records, unless the change is due to causes attributable to the FPC.


Refund of tuition fees

The FPC reserves the right to cancel or postpone a study due to a lack of participants. Affected participants may choose between participating in another study or requesting a refund of the amount paid within one month of notification by the FPC. If no response is received, the amount paid will be used to support other students. The FPC will not provide any additional compensation and/or indemnification in the event of cancellation or postponement of a study programme or changes in its delivery. In the event that the FPC makes changes that do not substantially affect the content of the study programme, the place of delivery, the timetable and/or the start date, the participant will not be entitled to a refund of the registration fee or any additional compensation.


Non-payment of tuition fees

Failure to pay the total or partial amount of the tuition fees within the established deadlines may result in the suspension or termination of the training service under the terms indicated below. The FPC is authorised to take whatever action it deems appropriate to suspend the service; on the one hand, in the academic sphere, by suspending the academic record, denying access to teaching in the classroom (face-to-face or online), limiting access to the virtual campus, not assessing any of the subjects and making it impossible to continue with internship agreements, among other measures; and on the other hand, in the administrative and legal sphere, by undertaking the corresponding claims and actions for compensation.

Students who have outstanding debts with the FPC or who have not passed all the credits necessary to complete their studies before the end date of the course will not be able to obtain their degree or certificate, as applicable. Individuals with outstanding amounts payable to the FPC will not be able to enrol in any new studies offered by the FPC until the outstanding amount has been paid.

Finally, the FPC reserves the right to permanently suspend enrolment (automatic withdrawal), without any obligation to refund any amount, in the following cases:

  • Lack of accuracy and/or validity of the information and documentation provided and failure to respond to documentation requirements;
  • Non-payment of part or all of the enrolment fee within the agreed deadlines;
  • Engaging in any behaviour, expression or content that is defamatory, illegal, offensive or that undermines the values and dignity of individuals (teachers, participants, management staff, etc.) or the good image and reputation of the FPC, whether in physical or virtual environments, including social networks.

Right of withdrawal

Participants in a study may exercise their right of withdrawal for a period of 14 calendar days from the date of enrolment, provided that the study has not yet begun. Therefore, by reading and accepting these Terms and Conditions, participants are informed that once the course has begun or is in progress, the right of withdrawal no longer applies, in accordance with the provisions of Article 103 a) of Law 3/2014 of 27 March, which amends the revised text of the General Law for the Defence of Consumers and Users and other complementary laws, approved by Royal Legislative Decree 1/2007 of 16 November, and related regulations.


Teaching

The place and/or date of teaching may change for academic reasons (changes and adjustments to the calendar, need for additional teaching resources, etc.) and for organisational and logistical reasons (adaptation of spaces). Participants will be notified of any such changes at least 15 calendar days before the start of the course. Specific and temporary changes will be notified in advance.


Right to a degree/certificate

Upon completion of a continuing education master's degree, specialisation diploma or expertise diploma, students are entitled to receive a degree, issued by the rector of the University, for students with a previous university degree equivalent to level 2 of the Spanish Qualifications Framework for Higher Education (MECES), according to a standardised model. Students who do not provide proof of their university degree are entitled to obtain a certificate from the FPC, in accordance with a standard model. Short courses, with a workload of less than 15 ECTS credits, entitle students to obtain a certificate issued by the FPC, according to a standard model; however, if these courses include so-called micro-accreditations, students will obtain a digital accreditation issued by the University and recognised in the countries participating in Europass or equivalent. Degrees are issued in Catalan and English and, at the student's request, in Spanish and English. In the case of joint degrees, the issuance must comply with the provisions of the corresponding collaboration agreement.


Accreditation of university qualifications and other documents

If the required documentation is not not submitted before the last day of study for those degrees that require it, or if it is not authentic and/or sufficient, the degree will not be issued, even if the participant has passed the course.


Dispute resolution

The training services provided by the FPC are, in all cases, subject to private law. Any interpretation or divergence arising from these Terms and Conditions shall be the responsibility of the FPC. In the event of disagreement, the dispute shall be subject to private law and the courts of the city of Barcelona with ordinary civil jurisdiction, with express waiver of any other jurisdiction that may apply.


Belongings in the event of theft

Neither the FPC nor its staff shall be liable for any loss, damage or theft of any type of personal or similar items carried by participants or other occasional users of the facilities, who must pay special attention to their belongings at all times.


Organisation of teaching in exceptional circumstances

The FPC organises teaching in a flexible environment that allows it to adapt to any unforeseen circumstances that may arise, as well as to any regulations that may be established by the authorities. If at any time the authorities (university, health or any other competent body) recommend limiting face-to-face teaching as much as possible, the FPC, in coordination with these authorities, will take the necessary measures to implement this recommendation, and, as a result, teaching activities may become 100% online during the period established in the relevant recommendation, without the need to declare a state of emergency and/or suspend face-to-face teaching activities and/or implement formal lockdown or mobility restriction measures.


Barcelona, 19 May 2026


Send