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Big Data Management, Technologies and Analytics

Master's degree. Face-to-face.

Presentation

2nd EDITION
UPC School

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 organisations' operations, and requires the digitalisation 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 digitalisation of an organisation is an arduous task, the data generated and collected can be analysed in order to generate important information for making business decisions. This has now been identified as a determinant and differentiating success factor that increases organisations' 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 (digitalisation 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 organisations, 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 organisation 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.

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Aims

  • 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

  • Information technology professionals (i.e. with a degree in computer science or equivalent) interested in retraining in the field of Big Data.
  • The typical information technology roles this master's degree is aimed at are developers, architects, data analyst and systems administrators.
  • The programme is focused on creating mixed profiles (Big Big Data Analytics and Data Management), meaning that technical training in centralized databases and programming and basic knowledge of statistics is required (equivalent to the knowledge obtained in any Engineering degree).

Content

Subjects

Data Management
12 ECTS. 72 teaching hours.
1. 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.

2. Basic principles of non-relational databases (NoSQL).
- New architectures.
- New data models.

3. 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.

4. 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).

5. 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.

6. Data integration.
- Intensive data processes and ETLs.
- Polystores and multilingual systems .
- Orchestrators: Muskeeter.

7. Visualisation.
- Visualisation processes.
- Visualisation techniques.
Data Analytics
12 ECTS. 72 teaching hours.
1. Introduction.
- What is knowledge discovery?
- Basic statistics.
- Introduction to R.

2. Pre-processing of data.
- Data cleansing and adjustment.
- Transformations.

3. Basic analysis techniques.
- Multiple regression.
- Profiling.

4. Multivariate Analysis.
- Principal component analysis.
- Clustering.
- Decision trees.

5. Machine Learning.
- Concept.
- Mathematical foundations.

6. Main machine learning techniques.
- Association rules.
- Supervised linear methods.
- Neuronal networks.
- Support vector machines.
- Random forests.

7. Text processing.
- Pre-processing and preparation of data.
- Main text mining techniques.
- Information retrieval.

8. Time series analysis.
- Pre-processing and preparation of data.
- Forecasting.
- Identifying outliers.

9. Advanced data analysis.
- R packages for parallel processing.
- R and relational databases.
- Data analysis in distributed environments using HDFS and Spark.
* Spark R.
Hands-on Experience: Data Maganement and Analytics
16 ECTS. 96 teaching hours.
1. Infrastructure.
- Introduction to Cloud environments.
- Virtualisation.
- Oracle services.

2. Distributed storage.
- The Hadoop Ecosystem.
- Key-Value Systems: HBase.

3. Distributed processing.
- MapReduce.
- Spark.
* SparkSQL.
* Spark Streaming.
* Spark Graphs.

- Data analysis in distributed environments.
* MLlib.
* SparkR.

4. Document Stores.
- MongoDB.
- ElasticSearch.

5. Graph databases.
- Neo4J.
- Semantic graphs: GraphDB and SPARQL.

6. Big Data systems architecture.
Business and Entrepreneurship in Big Data
5 ECTS. 33 teaching hours.
1. Introduction: The competitive environment of the company and Big Data.
- Big Data Landscape.

2. Success stories.

3. Business ideation techniques.
- Clients and users.
- Definition of products and services.

4. Business modelling tools: Business Model Canvas.
- Constituent parts.
- Practical cases.
- Resolution of cases: Twitter, Facebook, etc.

5. Financing process.
- Finance.
- Private funding.
* Business Angels.
* Venture Capital.

- Public funding.

6. Marketing.

7. Creating a business.
- Legal issues.
* Data regulation

- Financial considerations.

8. Ethical considerations of Big Data: Business and Privacy.

9. Presentations and pitch.
Project
15 ECTS. 45 teaching hours.
1. Project management.
- Agile methodologies.
- Specific considerations for Big Data.

2. Presentation of the project.

3. Project monitoring sessions.
The UPC School reserves the right to modify the contents of the programme, which may vary in order to better accommodate the course objectives.

Management & Faculty

Academic management

  • Abelló Gamazo, Alberto
    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 (Master in Innovation and Research in Informatics - Data Mining and Business Intelligence). UPC coordinator of the Erasmus Mundus Master's Degree in Information Technologies for Business Intelligence. He has worked as a consultant with SAP, HP and the WHO, among others.

Teaching staff

  • Abelló Gamazo, Alberto
    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.
  • Batlle Maymó, Adrià
    Computer Engineer. Venture Builder Director at InnoCells (by Banco Sabadell)
  • 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 by the UPC. He has worked for more than 10 years in different companies in the security and defense sector. He currently 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
    Major in Informatics Engineering (2007), Master in Computer Architecture (2008), and Doctor by the Universitat Politècnica de Catalunya, speciality in Computer Sciences (2013). 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 (2014 - today). Previously, he's been working in the "High Performance Computing" research group (HPC-UPC) (2007-2009), the "Relational Algorithms, Complexity and Learning" research group (LARCA-UPC) (2009-2013), and in the industry at Systelab Technologies (2014). His principal interests are Machine Learning, Data Mining, Artificial Intelligence, and Cloud Computing.
  • Cebrián Chuliá, Antonio
    MSc in Artificial Intelligence from the UPC. He has 15 years of experience developing software in telecommunications and Internet companies. He has exercised its task of Data Scientist at companies such as Telefonica, Tuenti, Softonic... Actually he works in Enerbyte, startup specializing in the analysis of power consumption data to generate intelligent recommendations for energy saving, where he is Chief Data Officer (CDO).
  • Delicado Useros, Pedro Francisco
    Associate Professor in the Department of Statistics and Operative Research of the UPC. Coordinator UPC of the Master in Statistics and Operational Research UPC-UB (MESIO UPC-UB). 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).
  • Escribano Cambronero, Marc
    Ingeniero Superior en Informática por la Facultad de Informática de Barcelona (UPC). Máster en BigData Management & Analytics por la UPC School of Professional & Executive Development. Actualmente Head of Data & Analytics Manager en Desigual. Anteriormente Business Intelligence Manager en Desigual, Business Intelligence Analyst en Finconsum "la Caixa", Consultor Business Intelligence. Ponente en eventos nacionales e internacionales explicando cómo posicionar un data warehouse con datos real-time en el centro de una organización multinacional.
  • García del Poyo Vizacaya, Rafael Emiliano
    Abogado y socio de Digital Business de Osborne Clarke.
  • González Alonso, Pedro
    Computer Engineer by 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.
  • Gutiérrez Torre, Alberto
    Licenciado en ingeniería informática por la UPC. Actualmente estudiante del máster MIRI con mención en Data Mining and Business Intelligence. Trabajó en la creación de la plataforma TextServer para el grupo de investigación TALP de la UPC.
  • Jamin, 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
    Engineer 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 by the UPC and Diploma in Business by 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
    Computer Engineer by UPC. Master IT4BI (Information Technologies for Business Intelligence). He has worked as a consultant and researcher in BI and Big Data to Incubio
  • Pradel Miquel, Jordi
    The holder of a degree in computer engineering from the Universitat Politècnica de Catalunya (UPC). Ex-Associate lecturer in the Department of Service and Information System Engineering at the 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 the 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 (Master in Innovation and Research in Informatics - Data Mining and Business Intelligence). UPC coordinator of the Erasmus Mundus Master's Degree in Information Technologies for Business Intelligence. 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 Conector Startup Accelerator
  • Touma, Rizkallah
    MSc in Information Technology for Business Intelligence (IT4BI) between the Free University of Brussels (ULB), Polytechnic University of Catalonia (UPC) and François-Rabelais University of Tours (UFRT). Degree in Computer Science from the University of Damascus, Syria (2007). He is currently a researcher with the Storage Systems Group in Barcelona Supercomputing Center (BSC).
  • Varga, Jovan
    MSc in Computer Science from the UPC. PhD student IT4BI-DC (Information Technologies for Business Intelligence Doctoral College) at the Polytechnic University of Catalonia and the University of Aalborg. His research is in the area of Business Intelligence and Semantic Web.
  • Vázquez Alcocer, Pere-Pau
    PhD in software by 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.
  • Verdejo Álvarez, Gabriel
    Ingeniero Superior en Informática por la UAB. Ha trabajado en diversas empresas del sector TIC de la innovación. Actualmente, es responsable del RDlab, Laboratorio de Investigación y Desarrollo en la UPC. (rdlab.cs.upc.edu). Trabaja en la gestión de la investigación, desarrollo e innovación en ámbitos TIC.

General information

Credits
60 ECTS (318 teaching hours)
Start date
Classes start:08/10/2018Classes end:12/07/2019Programme ends: 08/11/2019
Timetable
Monday  18:00 to 21:00Wednesday  18:00 to 21:00Friday  18:00 to 21:00
Taught at
Facultat d'Informàtica de Barcelona (FIB)
C/ Jordi Girona, 1-3
Barcelona
Contact
Telephone: (34) 93 114 68 05
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 or a university qualification equivalent to an EHEA degree, diploma or degree. Otherwise, the student will receive a certificate of completion of the programme issued by the Fundació Politècnica de Catalunya.

In the case of having a foreign degree check here.
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.
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 appear annually of the UPC School of Professional & Executive Development Employment service .The offers range from formal contracts to work placement agreements.
Registration fee
8.500 €
See the section Discounts, loans and financial aid for possibilities of advantageous financing conditions.

Applicants are given the option of making a voluntary €5 contribution when formalising their enrolment. As part of the UPC's 0.7% Campaign, this donation will go towards meeting charitable needs in developing countries.

0.7%

Language of instruction
Spanish
Payment of enrolment fee
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

Related entities

Collaborators