The aim of the Erasmus Mundus master in Data Mining and Knowledge Management (DMKM) in Data Mining and Knowledge Management is to train engineers and researchers to extract latent, potentially useful information from stored data, display it to the final user in a comprehensible manner and incorporate it into an intelligent decision-making system. Beginning with basic training, students can choose two specialisations from among the following six: E-Science; Data Mining in the Social Sciences; Knowledge and Decision Making; Statistical Modelling and Data Mining; the Semantic Web; and Relational Data Mining.
|Duration||Two academic years|
|European programme||Erasmus Mundus|
|Languages of instruction||English|
|Organised by||Barcelona School of Informatics (FIB)|
Universitat Politècnica de Catalunya (UPC)
Ecole Polytechnique de l'Université de Nantes (France)
Università degli Studi del Piemonte Orientale Amedeo Avogadro (Italy)
Universitatea Politehnica din Bucuresti (Romania)
Université Lumière Lyon 2 (France)
Université Pierre et Marie Curie (France)
|Coordinating university||Université Lumière Lyon 2 (France)|
Facultat d’Informàtica de Barcelona (FIB).Edifici B6. C. Jordi Girona, 1-3. 08034 Barcelona
The Erasmus Mundus Masters fees are determined by the University Consortium that organizes the studies. These fees are the same for all the participant universities. For more information, please do not hesitate to contact the master web site.
On finishing the master's degree, graduates will be able to:
Transversal competences are those things that the graduate will be able to understand or do upon completion of the learning process, regardless of the specific course. The transversal competences established by the UPC are: capacity for innovation and entrepreneurship, sustainability and social commitment, knowledge of a foreign language (preferably English), teamwork and proper use of information resources.
- Analyse in depth information requirements for problem solving.
- Manage large databases.
- Extract hidden information and knowledge from these databases.
- Implement this knowledge in decision-making support systems and intelligent systems.
Graduates of this master degree have the opportunity to work in areas in which data are important for acquiring new knowledge and making decisions, such as the high-tech industry, business intelligence services, web analysis and management, banking and finance, public institutions (particularly in the fields of health, transport and governance), academic environments and research, particularly genomic and medical research.
|General requirements||Academic regulations|
Candidates will be ranked according to their score. The top 60 candidates will be invited for a personal interview, from which 30 candidates will be chosen for admission. A waiting list of 10 more candidates will be drawn up to cover possible withdrawals from the course.
To enrol for an interuniversity Master’s degree coordinated by a university other than the UPC, you must enrol through the coordinating university:
Université Lumière Lyon 2 (France)
|Advanced Statistical Modelling||4||Optional|
|Bioinformatics and Statistical Genetics||4||Optional|
|DMKM Case Study||4||Optional|
|Kernel-Based Learning and Multivariate Modelling||4||Optional|
|Logic & Knowledge Representation||4||Compulsory|
|Methodology and Tools for Research||4||Compulsory|
|Multidimensional Data Analysis||4||Compulsory|
|Numerical Machine Learning||4||Compulsory|
|Probability and Statistics||4||Compulsory|
|Statistical Processing of Natural Language||4||Optional|
|Symbolic Machine Learning||4||Compulsory|
|Bioinformatics - Sequences, Tree and Graph Mining||4||Optional|
|Case Study (Epun)||4||Optional|
|Case Study (Uly2)||4||Optional|
|Case Study (Upmc)||4||Optional|
|Complex Data Warehousing||4||Optional|
|Data Processing: Cleaning, Feature Selection, Feature Construction||4||Optional|
|Epistemology and History of Science||4||Optional|
|Mining Complex Data: Text, Image, Web||4||Optional|
|Modelling Complex Systems in Social Science||4||Optional|
|Ontology Engineering and Semantic Web||4||Optional|
|Opinion Mining and Technological Watch||4||Optional|
|Relational Pattern Mining||4||Optional|
|Scientific Discovery and Creativity||4||Optional|
|Visual Data Mining||4||Optional|
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