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Start   >  Master's & postgraduate courses  >  Education  >  Continuing education master's degree in Big Data Management, Technologies and Analytics
We advise you! Request information or admission
  • discount

    BENEFIT FROM SPECIAL TERMS ON REGISTRATION BEFORE JULY 1!

    ASK YOUR PROGRAM ADVISOR!

Presentation

Edition
8th Edition
Credits
60 ECTS (324 teaching hours)
Delivery
Face-to-face
Language of instruction
Spanish
Fee
€8,900
Special conditions on payment of enrolment fee and 0,7% campaign
Take advantage of the special enrollment conditions in this admission round! Complete your enrollment by July 1. Ask your Program Advisor!.
Start date
Classes start: 07/10/2024
Classes end: 11/07/2025
Programme ends : 24/11/2025
Timetable
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 continuing education master's degree?
The importance of data in today's society is unquestionable. A large proportion of companies - those known as digital companies - base their business model on the collection, storage and analysis of any data relevant to their business. This philosophy implies a radical change in the management of organizations' operations, and requires the digitalization of all their business processes (e.g. creating computer systems to interact with customers and suppliers by websites, mobile applications or GPS systems, adding sensors to mechanical processes to monitor them, etc).

While the digitalization of an organization is an arduous task, the data generated and collected can be analyzed in order to generate important information for making business decisions. This has now been identified as a determinant and differentiating success factor that increases organizations' competitiveness.

Today, the term Big Data is used to refer to a new type of systems that gather and analyse all kinds of data, and the challenges they entail. The most popular definition of the term Big Data is based on the three Vs, which represent its three main challenges: volume (digitalization of some processes can generate large volumes of data) variety (from heterogeneous data sources) and velocity (in terms of potential arrival time and data processing in real time). Today, to address these three major challenges, Big Data is based on two cornerstones: new architectures (mainly based on Cloud Computing and distributed and memory data management) and new data models (such as documents, graphs, key-value and streams).

However, the barrier to entry for incorporating Big Data solutions remains very high for most organizations, as they are managed and maintained in a very different way from any other system. Furthermore, the tools currently used are not yet mature and require a high degree of expertise if they are to be used properly. For this reason, specialization in this field involves specific recycling based on the main concepts behind these technologies. Today, it is necessary to make a distinction between data management in Big Data systems (Big Data Management) and using these data to extract knowledge relevant to the organization with Data Mining and Machine Learning algorithms (Big Data Analytics). In addition, there is no universal solution for either management or exploitation that can be easily replicated in any field, since by definition, the solution in these environments depends on the specific case of use (exploitation).

This master's degree in Big Data Management, Technologies and Analytics therefore provides an overview of the Big Data ecosystem and an in-depth examination of both aspects: management (Big Data Management) and exploitation of data (Big Data Analytics), while providing applicability and a business vision within this world.
Aims
  • Understand the Big Data management problem and its specificities.
  • Identifying the most important characteristics in Big Data management which govern the choice of an architectural solution.
  • Understanding the open data paradigm.
  • Practising with the main Big Data management tools currently on the market (Hadoop, MongoDB, Neo4J, GraphDB, Spark, etc.).
  • Understanding when a business problem can be formalized as a machine learning problem.
  • Identifying the statistical or machine learning models that are most suitable for a given problem.
  • Being able to perform pre-processing of data (structured or unstructured).
  • Being able to evaluate the success rate of the proposed models.
  • Acquiring specific knowledge about the use of Big Data for decision-making in business.
  • Identifying best practices in the application of Big Data when creating a business.
  • Using business modelling tools.
  • Understanding the economic, ethical and legal principles of the operation of a business
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
  • Motivation and introduction
    • Context. The data society and the data-driven paradigm.
    • Cases of use.
    • Cloud computing and services engineering (XaaS).
    • The need for a paradigm shift: NoSQL.
  • Basic principles of non-relational databases (NoSQL)
    • New architectures.
    • New data models.
  • Foundations: new architectures
    • Basic concepts.
    • One size does not fit all.
    • Main reference architectures.
  • Foundations: new data models
    • Basic concepts.
    • Unstructured and semi-structured data models.
    • Main data models in the NoSQL world: Key-Value, Document-oriented, Graphs, Semantic Graphs and Streams.
    • Advanced data modelling (for non-relational systems).
  • Main families of NoSQL managers
    • Key-Value Managers
      • Concept and principles.
      • The Hadoop ecosystem: HDFS and HBase.
      • Specific modelling considerations.
    • Document-oriented managers
      • Concept and principles.
      • Example: MongoDB and the Aggregation Framework.
      • Specific modelling considerations.
    • Column-oriented managers
      • Concept and Principles
      • Example: Arrow (database) and Parquet (files).
      • Specific modelling considerations.
    • Graph managers
      • Concepts and principles.
      • Types of graphs and operations.
      • Example: Neo4J and Cypher.
      • Specific modelling considerations.
    • Semantic graph managers
      • Concept and Principles: the paradigm of Open Data / Linked Data.
      • How to open data.
      • Architectures based on graphs vs. relational technology.
      • RDF and SPARQL.
      • Example: GraphDB.
      • Specific modelling considerations.
  • Distributed and parallelizable data processing
    • Management and distributed processing of data.
      • MapReduce.
    • Management and processing of data in memory.
      • Spark.
  • Data integration
    • Intensive data processes vs. ETLs.
    • Data Lake concept, multilingual systems.
    • Orchestrators.
  • Big data architectures
    • Main reference architectures.
      • Lambda architecture.
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 course objectives.
Degree
Continuing education master's degree in Big Data Management, Technologies and Analytics, issued by the Universitat Politècnica de Catalunya. Issued by virtue of the provisions of art. 7.1 of Organic Law 2/2023 of 22 March, concerning the University System, and art. 36 of Royal Decree 822/2021 of 28 September, which establishes the organisation of university education and the procedure for ensuring its quality. A prior official university qualification is necessary to obtain it. Otherwise, the student will receive a certificate of completion of the programme issued by the Fundació Politècnica de Catalunya. Lifelong learning studies at the Universitat Politècnica de Catalunya are approved by the University's Governing Council on an annual basis. (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 course'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
  • Abelló Gamazo, Alberto
    info
    View profile in futur.upc / View profile in Linkedin
    Holds a doctorate in Computer Science from the Polytechnic University of Catalonia (UPC). A lecturer in the Department of Service and Information System Engineering at the UPC. He teaches at both bachelor's degree level and on the Master's degree course in Innovation and Research in Informatics (MIRI), specialising in Data Science. He is the UPC coordinator of the Erasmus Mundus doctorate in Information Technologies for Business Intelligence - Doctoral College (IT4BI-DC).

    Furthermore, he has worked as a consultant with SAP, HP and the OMS, Fundació Probitas,  among others.
  • 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.
  • 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 worked for more than ten years in different companies in the security and defense sector. Currently, he works at aunav (within the NTT Data group), in the aunav robots division, dedicated to the development of explosive deactivation robots, as head of the software and artificial intelligence department.
  • Berral García, Josep Lluís
    info
    View profile in futur.upc
    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.
  • Delicado Useros, Pedro Francisco
    info
    View profile in futur.upc / View profile in Linkedin
    Professor in the Department of Statistics and Operative Research at Universitat Politècnica de Catalunya (UPC). Author of more than 45 international papers, his research topics include unsupervised learning (interpretability in machine learning, main curves, clustering, multidimensional scaling), functional data analysis (spatial dependence, principal components) and applications (demography, analysis of electoral results, bioinformatics). He has collaborated as a statistical consultant with SEIF-88 (clinical trials) and the Agència per a la Qualitat del Sistema Universitari de Catalunya (sampling).
  • Deulofeu Aymar, Joaquim
    info
    View profile in futur.upc / View profile in Linkedin

    Holds a degree and PhD in Economic and Business Sciences from the University of Barcelona (UB). He is an EFQM international evaluator-advisor, and the founding partner and CEO of Qualitat, Serveis Empresarials, S.L., the company where he has been working as a consultant for more than twenty-five years. Associate professor at the Business Organization Department of the Universitat Politècnica de Catalunya (UPC), and professor of the UPC School.

  • 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 Universitat Politècnica de Catalunya (UPC) and master's degree in Artificial Intelligence from the UPC. He is currently a researcher at the UPC's Department of Signal Theory and Communications and at the Barcelona Supercomputing Center (BSC), as well as an associate professor at the UPC's Department of Computer Science. His area of expertise is natural language processing, especially multilingual machine translation with neural networks.
  • Flores Herrera, Javier de Jesus
    info
    View profile in Linkedin
    The holder of an Engineering degree in Software Development from the Polytechnic University of Chiapas (UPCH). He is currently a student on the master's degree in Innovation and Research in Informatics, specialising in data science at the Universitat Politècnica de Catalunya (UPC). He works with the Database Technologies and Information Management (DTIM) research group, and has worked as a developer and on support for business microservices at IBM.
  • 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 and master in Innovation and Research in Computer Science from Universitat Politècnica de Catalunya (UPC), specializing in Business Intelligence and Knowledge Discoverer. Master in Business Administration at ESADE. Currently, he works as Head of Data Science and CTO at nixi1, where he is leading the development of the chatbot technology and the nixi1 multi-channel solution platform.
  • Gutiérrez Torre, Alberto
    info
    View profile in Linkedin
    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.
  • Jamin Jean Jacques, Emmanuel
    info
    View profile in Linkedin
    The holder of a doctorate in Computer Science from the University of Paris - XI. Research engineer in several European projects in the sphere of the semantic web (FP7 projects: SevenPro, IntelLEO, Neon, Khresmoi, MultiSensor and Horizon 2020 projects: Socatel, Roborder, Aqua3S, Welcome). He is currently an IT consultant at Everis on artificial intelligence issues, particularly the semantic web and automatic natural language processing (NLP) techniques for knowledge management. He also has experience in creating the startup Open Data Consulting, where he was Chief Technology Officer (CTO).
  • 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.
  • 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.
  • Palmer, Jonathan
    info
    View profile in Linkedin
    Vice President of the Scopely data platform. Holds a Bachelor of Arts degree in Ancient History from the University of Bristol. He has more than twenty years of experience in software engineering and big data, and has worked in various industries, including mobile games, media and entertainment and fintech in London and Barcelona.
  • 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.
  • Torrent Moreno, Marc
    info
    View profile in Linkedin
    Holds a degree in Telecommunications Engineering from the Polytechnic University of Catalonia (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 United States (British Telecom, NEC Deutschland, Mercedes-Benz United States, University of California, Ficosa International and Eurecat). He focuses on the world of data, 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.
  • 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

Contact:
Cecilia Salas Silva
(34) 93 706 80 35
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After we have registered your request, you will receive confirmation by email and we will be in touch.

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  • If you have any doubts about the continuing education master's degree.
  • 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. Validate your curriculum vitae and 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.





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