Universitat Politècnica de Catalunya · BarcelonaTech

Erasmus Mundus Master in Data Mining and Knowledge Management (DMKM)

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.

Starting September
Duration Two academic years
ECTS credits 120
Delivery Face-to-face
European programme Erasmus Mundus
Languages of instruction English
Organised by Barcelona School of Informatics (FIB) Obre en finestra nova
Participating institutions Universitat Politècnica de Catalunya (UPC) Obre en finestra nova
Ecole Polytechnique de l'Université de Nantes (France) Obre en finestra nova
Università degli Studi del Piemonte Orientale Amedeo Avogadro (Italy) Obre en finestra nova
Universitatea Politehnica din Bucuresti (Romania) Obre en finestra nova
Université Lumière Lyon 2 (France) Obre en finestra nova
Université Pierre et Marie Curie (France) Obre en finestra nova
Coordinating university Université Lumière Lyon 2 (France) Obre en finestra nova
Location

Facultat d’Informàtica de Barcelona (FIB).Edifici B6. C. Jordi Girona, 1-3. 08034 Barcelona

Prices

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.

Website http://www.em-dmkm.eu/ Obre en finestra nova
E-mails info.masters@(fib.upc.edu)
info.emdmkm@(fib.upc.edu)


On finishing the master's degree, graduates will be able to:

Transversal competences

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.

Specific competencies

  • 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 Obre en finestra nova
Specific requirements
  • Holding a bachelor's degree (a course of study of at least three years’ duration at university, equivalent to 180 ECTS credits) in Informatics, Applied Mathematics, Statistics or a related discipline such as engineering or physics.
  • An English language level equivalent to a TOEFL score of 550.
Admission criteria
  • The quality of academic results in the last three years (20 points).
  • The relationship between the candidate's prior learning and the content of the master's degree (10 points).
  • Practical experience (2 points).
  • Knowledge of foreign languages other than English (2 points).
  • Reports issued by at least two lecturers who have known the candidate personally regarding their suitability for the master's degree (5 points).
  • Motivation and personal goals (20 points).
  • Oral expression and communication skills (16 points).

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.

Entry places 20
Pre-enrolment 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) Obre en finestra nova
Subjects ECTS credits Type
First semester
Advanced Databases 4 Compulsory
Advanced Statistical Modelling 4 Optional
Bioinformatics and Statistical Genetics 4 Optional
DMKM Case Study 4 Optional
Information Retrieval 4 Compulsory
Kernel-Based Learning and Multivariate Modelling 4 Optional
Language S1 6 Compulsory
Language S1 6 Compulsory
Language S1 6 Compulsory
Language S2 2 Compulsory
Language S2 2 Compulsory
Language S2 2 Compulsory
Language S3 2 Compulsory
Logic & Knowledge Representation 4 Compulsory
Methodology and Tools for Research 4 Compulsory
Multidimensional Data Analysis 4 Compulsory
Numerical Machine Learning 4 Compulsory
Optimization 4 Compulsory
Probability and Statistics 4 Compulsory
Statistical Processing of Natural Language 4 Optional
Symbolic Machine Learning 4 Compulsory
Second semester
Bayesian Networks 4 Optional
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
Third semester
Master Thesis 30 Project
Fourth semester
Software Methodologies 4 Compulsory

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