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Start   >  Master's & postgraduate courses  >  Education  >  Master's degree in Big Data Management, Technologies and Analytics
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  • Programme
  • Training Content
  • Learning methodology
  • Teaching team
  • Associates entities
  • Career opportunities
  • Request information or admission

Programme

Edition
5th Edition
Credits
60 ECTS (324 teaching hours)
Delivery
Face-to-face
Language of instruction
Spanish
Fee
€8,600
Payment of enrolment fee options

The enrolment fee can be paid:
- In a single payment to be paid within the deadline specified in the letter of admission to the programme.
- In two instalments:

  • 60% of the amount payable, to be paid within the deadline specified in the letter of admission to the programme.
  • Remaining 40% to be paid up to 90 days at the latest after the starting date of the programme.
- In four instalments, splitting the payment by direct debit:
  • 40% of the amount payable, to be paid within the deadline specified in the letter of admission to the programme.
  • The remaining 60% will be divided into 3 direct debit payments, which will be distributed equidistantly between the beginning and end of classes.
  • The student must have and be the holder of a bank account with IBAN ESXX
Notes 0,7% campaign

Registration open until the beginning of the course or until end of vacancies.
Discounts, loans and financial aid
Start date
Classes start: 13/10/2021
Classes end: 15/07/2022
Programme ends : 24/11/2022
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 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 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 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.
      • Example: GraphDB.
      • Specific modelling considerations.
  • Data integration
    • Intensive data processes vs. ETLs.
    • Data Lake concept, multilingual systems.
    • Orchestrators.
  • 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 and MLlib.
16 ECTS 96h
Hands-on Experience: Data Maganement 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.
  • 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.
  • 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.
  • Elevator Pitch and presentations.
15 ECTS 51h
Project
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
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 course completion certificate of the programme issued by the Fundació Politècnica de Catalunya. (Ver datos que constan en el certificado).

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 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
    View profile in futur.upc
    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, big data Management systems and distributed databases.
  • Romero Moral, Óscar
    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 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
    Associate professor at Universitat Politècnica de Catalunya (UPC), where he obtained his PhD in 2002 and has been working as a lecturer and researcher. His areas of expertise are Databases, Data Warehousing and Big Data Management. He coordinated at UPC European Erasmus Mundus programmes, both master and PhD, and a Marie-Curie ITN, as well as H2020 projects and R&D agreements with Hewlett Packard, Zurich Insurance, SAP or the World Health Organization. He has successfully advised 8 PhD thesis, publishing more than 30 journal and 75 conference articles, as well as more than 10 book chapters (H-index 28 Google Scholar).
  • Aluja Banet, Tomàs
    View profile in futur.upc
    Lecturer in the Department of Statistics and Operations Research at Universitat Politècnica de Catalunya (UPC). 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
    View profile in futur.upc
    PhD in Artificial Intelligence from Universitat Politècnica de Catalunya (UPC). Professor of 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
    View profile in Linkedin
    Computer Engineer from the Universitat Politècnica de Catalunya (UPC). He has worked for more than 10 years in different companies in the security and defence sector. Currently, he works at Everis Aerospace and Defense, at the area of aunav robots, acting as the development manager of the software and artificial intelligence 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.
  • Bilalli, Besim
    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
    View profile in futur.upc
    Professor in the Department of Statistics and Operative Research at Universitat Politècnica de Catalunya (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 Agència per a la Qualitat del Sistema Universitari de 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.
  • Díaz Iriberri, José

    Doctor in Computing from the UPC. He is currently engaged in research in the field of graphic computing, and is a lecturer at the University of Vic. He was a post-doctoral researcher on the European Commission's Marie Curie scholarship programme in the Visual Computing group of CRS4 (Italy). His research focuses on the visualisation of scientific data, parallel computing and programming GPUs, and the design of applications in virtual and augmented reality environments.
  • Escolano Peinado, Carlos

    Master's degree in Artificial Intelligence from the Universitat Politècnica de Catalunya (UPC). Computer Scientist from UPC FIB, Currently, is a PhD at the Signal Theory and Communications department of UPC working on neural machine translation.
  • Galí Reniu, Ferran

    Degree in Computer Science from Universitat Politècnica de Catalunya. 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. Currently, he is collaborating with the University of Barcelona creating a new Master on Big Data Engineering.
  • González Alonso, Pedro Javier
    View profile in Linkedin
    Computer Engineer and Master in Innovation and Research in Computer Science from Universitat Politècnica de Catalunya, specializing in Business Intelligence and Knowledge Discoverer. Master in Business Administration at ESADE. Currently, he works as Head of Data Science en nixi1 (www.nixi1.com), where he is leading the development of the chatbot technology and the process and analysis of the company databases.
  • Jamin Jean Jacques, Emmanuel
    View profile in Linkedin
    Doctor in Informatics from Paris XI University. Research engineer in many European projects in the domain of the Semantic Web (projects FP7: SevenPro, IntelLEO, Neon, Khresmoi, MultiSensor and projects H2020: Socatel, Roborder, Aqua3S, Welcome). He has experience in create a startup Open Data Consulting, where he was CTO. Currently, IT Consultant at Everis.
  • Jovanovic, Petar
    View profile in futur.upc
    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, big data Management systems and distributed databases.
  • López Miralpeix, Miguel
    View profile in Linkedin
    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.
  • Montornés Solé, Jordi
    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
    View profile in futur.upc / View profile in Linkedin
    PhD in Informatics from Universitat Politècnica de Catalunya(UPC) i la Université Libre de Bruxelles (ULB). Master in Information Technologies for Business Intelligence from UPC and ULB. Currently, he is a post doctorate researcher and Lecture of the Department of Service and Information System Engineering at the UPC. His research focuses on Data and Information management.
  • Pradel Miquel, Jordi
    View profile in Linkedin
    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
    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 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
    View profile in Linkedin
    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 in 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
    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, especially for mobile devices. Currently, he is Head Operations at Scopely and professor at Polytechnic University of Catalonia, in business and finance subjects, in Degrees and Masters in Video Games and Master in Big Data Management.
  • Touma, Rizkallah
    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 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
    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.

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:
(34) 93 114 68 05
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  • If you have any doubts about the 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 Master's degree:
    Application letter, describing your background (training and experience) and the motivation to register in 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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