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Big Data Management and Analytics

Postgraduate course. Face-to-face.

Presentation

4th EDITION
UPC School
Shortly we will publish updated information about the new edition of this programme.

Today, competition has led companies to identify their ability to make informed decisions as a key to success. Technology has played a very important role in this regard, enabling us to record the operational data of the company's daily activities and to analyze it afterwards. These types of systems are known as decisional systems, and data warehouses are the most widely used architecture for implementing them. However, thanks in part to the success of this type of system, the paradigm of analysis is changing, and a new type of data is being created, which is no longer only associated with the company's day to day activities, but also considers its "environment": social networks, logs, open data, etc.

The requirements of these new data types differ from those of the old ones, and have shown the limitations of traditional architectural solutions. For all these reasons, the term Big Data is currently used to refer to this new type of system 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 (large volumes of data), variety (heterogeneous data sources) and velocity (referring to processing and response times).

To address these three major challenges, Big Data today is based on the principle of "divide-and-conquer" according to which the problems have been formalized into sub-problems that can be executed simultaneously. For this reason, most Big Data solutions are based on "Cloud Computing" and on developing distributed systems in the cloud.

Big Data systems are computer systems that are based on similar design plans to all the others. We can therefore talk about the management of data 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). Unlike traditional systems, however, there is not so much justification to separate the data exploitation management part, as there is no universal solution for storing data and exploiting them in a Big Data environment. Instead, the architectural solution depends on the specific case of use (exploitation) being considered.

This Postgraduate programme provides an overview of Big Data ecosystem and considers both aspects in depth: management (Big Data Management) and exploitation of data (Big Data Analytics), while providing applicability and a business vision within this system.

Aims

  • Understanding the problems of managing Big Data.
  • 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 postgraduate programme 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
3 ECTS. 24 teaching hours.
  • Introduction: Cloud computing and Services Engineering (XaaS)
  • Data management in Cloud Databases (NoSQL)        

                   - Theoretical principles of distributed systems
                   - The most widely used unstructured or semi-structured data models      
                   - Main types of managers: Key-Value, Document Stores, Graph DBs,      
                     Stream Processing.                                        
                   - Main query languages: SQL-like, MapReduce, etc.

  • Semantic data models

                     - The paradigm of Open Data / Linked Data
                     - RDF and SPARQL
  • How to  open data
  • Data integration and visualization.
Data Analytics
3 ECTS. 24 teaching hours.
  • Introduction: Basic statistics

                 - Profiling

  • Multivariate Analysis

                  - Principal Component Analysis
                  - Clustering
                  - Decision Trees: Classification and regression trees  

  • Machine Learning

                 - Association Rules 
                 - Supervised Methods                          
                 - Randomized forests                  
                 - Support Vector Machines


                                       

Hands-on Experience: Data Management and Analytics
5 ECTS. 40 teaching hours.
  • NoSQL tools

           - Hadoop Ecosystem
           - HBase
           - MongoDB
           - Neo4J

  • Query tools: MapReduce, Spark
  • Processing tools: Spark Stream
  • Open Data tools: Virtuoso
  • Data Analytics Tools: R-Studio, MLlib
  • Big Data systems architecture
Project and Entrepreneurship
4 ECTS. 32 teaching hours.
  • Introduction: The competitive environment of the company
  • Ethical considerations of Big Data: Business and Privacy
  • Business modelling tools: Business Model Canvas

              - Constituent parts
              - Practical cases
              - Resolution of cases: Twitter, Facebook, etc.

  • Creating a business

             - Legal issues
             - Financial considerations

  • Financing process

             - Private funding: Business Angels, Venture Capital
             - Public funding

  • Marketing: Introduction to Big Data projects
  • Presentation of the project
  • Monitoring the project

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. 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. 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. Entrepreneurship & Innovation Manager, Barcelona Mobile World Capital Foundation
  • 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.
  • 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).
  • García del Poyo Vizacaya, Rafael Emiliano
  • 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
  • Herrero Otal, Víctor
    Computer Engineer by UPC. Researcher in the Department of Engineering Services and Information Systems UPC working mainly on optimizing NoSQL databases, both from the adaptation of classical optimization techniques how the application of new techniques, with particular regard to the Hadoop environment.
  • 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.
  • 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
  • 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
  • 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.
  • Verdejo Álvarez, Gabriel

General information 2016-17 EDITION

Next course
October 2017
Credits
15 ECTS (120 teaching hours)
Timetable
Wednesday  18:00 to 21:00Friday  18:00 to 21:00
Taught at
Facultat d'Informàtica de Barcelona (FIB)
Contact
Telephone: (34) 93 112 08 65
Degree
Postgraduate diplomas issued by the Universitat Politècnica de Catalunya. To obtain this degree it is necessary to have an official or recognized university degree equivalent to a bachelor's degree or diploma. Otherwise, the Fundació Politècnica de Catalunya will only award them a a certificate of completion.

In the case of having a foreign degree check here.
Virtual Campus
The students on this Postgraduate course 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
3.600 €
The registration fee must be paid before the beginning of this Postgraduate course.
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

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