Data Science

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Note: this specialization was formerly known as Data Mining and Business Intelligence

About

The master’s degree equips graduates with solid knowledge and hands-on experience in techniques for managing, analyzing and extracting hidden knowledge from structured and unstructured big data ensembles and in building adaptive analytic systems that are able to exploit that knowledge in modern organizations. In particular, the master’s degree addresses the new challenges of the smart society boom: fraud detection, bioinformatics, information extraction from linked open data, real time analysis of sensor data and social networks, and customer relationship management.

Target

The specialization in Data Science is aimed at students from all over the world. Candidates must have a bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or related fields, as well as a good level of English, and they must be motivated to discover knowledge from data in the fascinating world of sciences and technologies.

 

Specialization coordinator

Person Department Email
Romero Moral, Oscar ESSI

Specialization teachers

Person Department Email
Abello Gamazo, Alberto ESSI
Aluja Banet, Tomas EIO
Arias Vicente, Marta CS
Balcázar Navarro, Jose Luis CS
Basta, Christine Raouf Saad TSC
Belanche Muñoz, Luis Antonio CS
Bilalli, Besim ESSI
Carmona Vargas, Jose CS
Castellano Palomino, Marta Janira EIO
Castro Pérez, Jordi EIO
Castro Rabal, Jorge CS
Corchero Garcia, Cristina EIO
Delicado Useros, Pedro EIO
Ferrer Cancho, Ramon CS
Gibert Oliveras, Karina EIO
Graffelman, Jan EIO
Heredia Cervera, F. Javier EIO
Jovanovic, Petar ESSI
Kostov, Belchin Adriyanov EIO
Martín Muñoz, Mario CS
Montero Mercadé, Lidia EIO
Nadal Francesch, Sergi ESSI
Padro Cirera, Lluis CS
Romero Moral, Oscar ESSI
Ruiz Costa-Jussa, Marta CS
Sánchez Espigares, Jose Antonio EIO
Valiente Feruglio, Gabriel CS
Vellido Alcacena, Alfredo CS

Subjects

Specialization compulsory subjects

Specialization complementary subjects

 

Elective
Mandatory
Final Master Thesis
Mandatory Common Course

 

Semester 1

Algorithmic Methods for Mathematical Models
(AMMM - 6 ECTS)

Data Analysis & Knowledge Discovery
(DAKD - 6 ECTS)

Data Warehousing
(DW - 6 ECTS)

Statistical Modelling and Design of Experiments
(SMDE - 6 ECTS)

Techniques and Methodology of Innovation and Research in Informatics
(TMIRI - 6 ECTS)

Seminar activities

Semester 2

Concurrence, Parallelism and Distributes Systems
(CPDS - 6 ECTS)

Machine Learning
(ML - 6 ECTS)

Multivariate Analysis
(MVA - 6 ECTS)

Open Data
(OD - 6 ECTS)

Specialization Elective

Seminar activities

Semester 3

Specialization Elective

Specialization Elective

Elective

Elective

Seminar activities

Semester 4

Master Thesis
(TFM - 30 ECTS)

Seminar activities

Seminar activities

The activities to obtain the SIRI credits can be done in any semester of the master's degree. Consult the detail of the seminars.

Seminars of Innovation and Research in Informatics
(SIRI - 6 ECTS)

Specialization Elective

Algorithmics for Data Mining
(ADM - 6 ECTS)

Advanced Human Language Technologies
(AHLT - 5 ECTS)

Advanced Statistical Modelling
(ASM - 6 ECTS)

Big Data Management
(BDM - 6 ECTS)

Bioinformatics and Statistical Genetics
(BSG - 6 ECTS)

Information Retrieval
(IR - 6 ECTS)

Kernel based Machine Learning and Multivariate Modelling
(KMLMM - 6 ECTS)

Optimization Techniques for Data Mining
(OTDM - 6 ECTS)

 

Elective

Interdisciplinary Innovation Project
(I2P - 6 ECTS)

Viability of Business Projects
(VBP - 6 ECTS)

It is also possible:

  • Recognize credits for "External Practices" or "Laboral and Professional Experience"
  • Take courses from other MIRI specialties
  • Take courses from other masters at the UPC