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Start   >  Master's & postgraduate courses  >  Education  >  Master's degree in Big Data Management, Technologies and Analytics
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  • discount
    This programme is part of the Employment Help grants programme


3rd Edition
60 ECTS (324 teaching hours)
Language of instruction
Notes payment of enrolment fee and 0,7% campaign
Registration open until the beginning of the course or until end of vacancies.
Start date
Classes start: 07/10/2019
Classes end: 10/07/2020
Programme ends : 06/11/2020
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
Why this programme?
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 analyze 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, specialisation 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.
  • 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, 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.
  • 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 to 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.
    • 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.
    • Distributed data management and processing.
    • Data management and processing in memory.
    • 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, HBase, MapReduce and Spark.
      • 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.
      • Specific modelling considerations.
  • Data integration.
    • Intensive data processes and ETLs.
    • Polystores and multilingual systems.
    • Orchestrators: Muskeeter.
  • Visualisation.
    • Visualisation processes.
    • Visualisation techniques.
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.
16 ECTS 96h
Hands-on Experience: Data Maganement and Analytics
  • Infrastructure.
    • Introduction to Cloud environments.
    • Virtualisation.
    • 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.
    • Neo4J.
    • Semantic graphs: GraphDB and SPARQL.
  • 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.
  • Successful case.
  • 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.
  • Financing process.
    • Finance.
    • Private funding: Business Angels and Venture Capital.
    • Public funding.
  • Marketing.
  • Creating a business.
    • Legal issues: Data regulation.
    • Financial considerations.
  • Ethical considerations of Big Data: Business and Privacy.
  • Presentations and pitch.
15 ECTS 51h
Project will be carried out in groups 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.
Special master's degree issued by the Universitat Politècnica de Catalunya. Issued pursuant to art. 34.1 of Organic Law 4/2007 of 12 April, amending Organic Law 6/2001 of 21 December, concerning Universities. To obtain it, is necessary to have an official university qualification. Otherwise, the student will receive a certificate of completion of the programme issued by the Fundació Politècnica de Catalunya.

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.
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
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 master's degree will have access to the My_Tech_Space virtual campus, an effective work and communication platform for students, lecturers and course directors and coordinators. My_Tech_Space allows students to find background material for their classes, to work in teams, ask their lecturers questions, consult their marks, etc.

Teaching team

Academic management
  • Abelló Gamazo, Alberto
    View profile in futur.upc / View profile in Linkedin
    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 (Master in Innovation and Research in Informatics - Data Mining and Business Intelligence). UPC coordinator of the Erasmus Mundus Doctorate in Information Technologies for Business Intelligence - Doctoral College. He has worked as a consultant with SAP, HP and the WHO, among others.
  • Romero Moral, Óscar
    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 in the specialization Data Science of Master in Information Research and Innovation (MIRI-DS). Researcher in the field of data and information management, in which he has published more than 50 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
    View profile in futur.upc / View profile in Linkedin
    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 (Master in Innovation and Research in Informatics - Data Mining and Business Intelligence). UPC coordinator of the Erasmus Mundus Doctorate in Information Technologies for Business Intelligence - Doctoral College. He has worked as a consultant with SAP, HP and the WHO, among others.
  • Aluja Banet, Tomàs
    Lecturer in the Department of Statistics and Operations Research at the UPC. Coordinator of the Erasmus Mundus Master's programme in Data Mining and Knowledge Management at the UPC, head of the LIAM (Laboratory of Information Analysis and Modelling), and a member of the inLab FIB - the laboratory of the Barcelona School of Informatics for the development of ICTs. He has authored more than 50 articles published in scientific journals and studies. He has worked as a statistical consultant for La Caixa, TNS-Sofres AM, the Statistical Institute of Catalonia, and Barcelona City Council, among others.
  • Belanche Muñoz, Luis Antonio
    Lecturer in the Department of Computer Science at the UPC. Teaching on the Bachelor's degree in Computer Engineering, specializing in Computation, on the Master's Degree in Innovation and Research in Informatics, specializing in Data Mining and Business Intelligence, and on the Master's Degree in Artificial Intelligence.
  • Berbegal Castelló, José
    Computer Engineer from the UPC. He has worked for more than 10 years in different companies in the security and defense sector. Currently, he works at Proytecsa Security S.L., a company dedicated to the development of EOD (explosive ordnance disposal) robots, acting as the development manager of the software department.
  • Berral García, Josep Lluís
    Informatics Engineering, Master in Computer Architecture, and PhD in Informatics from the Universitat Politècnica de Catalunya, speciality in Computer Sciences. His research focuses on data mining and machine learning applications, also automatic management of data-centre environments. Currently he is a post-doc in the Barcelona Supercomputing Center. Previously, he's been working in the "High Performance Computing" research group (HPC-UPC), the "Relational Algorithms, Complexity and Learning" research group (LARCA-UPC) and in the industry at Systelab Technologies. His principal interests are Machine Learning, Data Mining, Artificial Intelligence, and Cloud Computing.
  • Delicado Useros, Pedro Francisco
    Professor in the Department of Statistics and Operative Research at UPC. Author of more than 35 international papers, his research topics include unsupervised learning (principal curves, clustering, multidimensional scaling), functional data analysis (spatial dependence, principal components) and applications (Demography, Bioinformatics). He has collaborated as a statistical consultant with SEIF-88 (clinical trials) and AQU-Catalunya (sampling).
  • Deulofeu Aymar, Joaquim
    View profile in futur.upc / View profile in Linkedin
    Degree and PhD in Economics. Quality implementation consultant. Associate professor of the Business Organisation Department at UPC.
  • Gali Reniu, Ferran
    Head of Technology at Trovit, where he leads Big Data projects in an online company that aggregates more than 200M classified ads, and serve more than 2M user per day. He studied Computer Science at the FIB-UPC, and right now he is collaborating with the University of Barcelona creating a new Master on Big Data Engineering.
  • González Alonso, Pedro Javier
    Computer Engineer from the UPC. Master in Innovation and Research in Computer Science from UPC specializing in Business Intelligence and Knowledge Discoverer and Master in Business Administration at ESADE. Currently, he works as Analytics and Big Data architect in a startup company linked to the health sector.
  • Jamin Jean Jacques, Emmanuel
    Doctor in Informatics from Paris XI University. Research engineer in many European projects in the domain of the Semantic Web (SevenPro, IntelLEO, Neon, KHRESMOI). Currently, CTO of Open Data Consulting.
  • Jovanovic, Petar
    PhD in Computer Science from the University of Belgrade. MSc in Computer Science from the UPC. PhD student IT4BI-DC (Information Technologies for Business Intelligence Doctoral College) at the UPC and the Free University of Brussels. His research is in the area of Business Intelligence, bigdata Management systems and distributed databases.
  • López Miralpeix, Miguel
    Computer Engineering from the UPC and Diploma in Business from the UOC. Enterprise Architect of Oracle Consulting working on the deployment of Big Data Architectures in large clients both locally (Caixabank, Gas Natural Fenosa, etc.) and internationally (Banco Santander Rio in Argentina, CIMB in Malaysia, Generalli in Italy, etc.). He leads the Oracle Barcelona Big Data Competence Center. He has collaborated in the teaching of the official MIRI master's degree.
  • Montornés Solé, Jordi
    Computer Engineer by the UPC. He has worked since 2004 in companies like Caixa Catalunya, HP and Vueling. Since 2011 he's focused on Mobile development, both in the server and Android.
  • Nadal Francesch, Sergi
    View profile in futur.upc / View profile in Linkedin
    PhD in Informatics from UPC. Master IT4BI (Information Technologies for Business Intelligence) from UPC. Currently he is a postdoctorate researcher and Lecture in The Department of Service and Information System Engineering at the UPC. His research focuses on Data and Information management.
  • Pradel Miquel, Jordi
    The holder of a degree in computer engineering from the Universitat Politècnica de Catalunya (UPC). In 2005 he founded Agilogy, a company specialising in the agile development of custom-made software, where he helps combined teams from Agilogy and the client to successfully implement agile software development methodologies in technologically complex and rapidly changing environments, using functional programming and Scrum, Kanban and XP techniques, among others.
  • Queralt Calafat, Anna
    Doctor in Informatics from the UPC. Senior Researcher in the Storage Systems group at the Barcelona Supercomputing Center, working on management, sharing and reuse of large amounts of data. She was previously a lecturer and researcher in the Department of Service and Information System Engineering at the UPC.
  • Romero Moral, Óscar
    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 in the specialization Data Science of Master in Information Research and Innovation (MIRI-DS). Researcher in the field of data and information management, in which he has published more than 50 publications in conferences and international journals. He has worked as a consultant with SAP, HP and the WHO, among others.
  • Torrent Moreno, Marc
    Telecommunication Engineering from the UPC. Doctor in Computer Science from the University of Karlsruhe in Germany and Executive MBA from ESADE Business School. He has participated since 2001 in several research projects in various fields of ICT, as part of various companies and universities in Europe and USA (British Telecom UK, NEC Deutschland, Mercedes-Benz R + D USA, the University of California, Berkeley and Ficosa International). Currently, he is Director of the unit Big Data Analytics in BDigital-Eurecat and director of the Center of Excellence in Barcelona Big Data promoting the culture of data and providing innovative solutions to market.
  • Torrents Poblador, Pere
    Operations Manager at Scopely.
  • Touma, Rizkallah
    PhD in Computer Science from the UPC. MSc in Business Intelligence from the UPC and the Université Libre de Bruxelles. He is currently a knowledge analyst and engineer with the Semantic Business Unit (SEMBU) at Everis Spain. He has worked as a junior researcher with the Barcelona Supercomputing Center and has participated in several pan-European projects (BigStorage, SoCaTel, CORDIS).
  • Vázquez Alcocer, Pere-Pau
    PhD in software from the UPC. Currently, he has an associate professor position 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 UOC, or the University of Vic.

Associates entities

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.

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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.

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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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