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The curriculum of the FIB Master's Degree in Data Science was approved by the Faculty Board on 1st July, 2020. It is adapted to the European Higher Education Area (EHEA) and has a 120 ECTS credits:

  • 54 compulsory credits
  • 36 elective credits
  • 30 credits of Master's Thesis

Curriculum structure

The University Master's Degree in Data Science from the UPC is structured in 4 semesters. The first semester covers 30 compulsory ECTS; the second semester covers the remaining 24 compulsory ECTS and 6 elective ECTS; the third semester must cover the remaining 30 optional ECTS. The fourth and final semester is fully devoted to the master thesis.

Thus, the compulsory training requires 54 ECTS (equivalent to 9 subjects of 6 ECTS) divided into 3 fields:

  • Data Science Fundamentals (12 ECTS): Statistical Inference and Modeling (SIM), and Algorithmics, Data Structures and Databases (ADSDB). 
  • Data management (18 ECTS): Data Warehousing (DW), Big Data Management (BDM) and Semantic Data Management (SDM).
  • Data analytics (24 ECTS): Multivariate Analysis (MVA), Process-oriented Data Science (PODS), Machine Learning (ML) and Mining Unstructured Data (MUD).

The following is the presentation of the structure of the master's study plan:

Master's Thesis


Semester 1

Statistical Inference and Modeling
(SIM - 6 ECTS)

Algorithms, Data Structures and Databases

Data Warehousing
(DW - 6 ECTS)

Multivariate Analysis
(MVA - 6 ECTS)

Process-oriented Data Science

Semester 2

Big Data Management
(BDM - 6 ECTS)

Semantic Data Management
(SDM - 6 ECTS)

Machine Learning
(ML - 6 ECTS)

Mining Unstructured Data
(MUD - 6 ECTS)

Semester 3

Semester 4

Master's Thesis
(30 ECTS)

Elective Courses

The elective training is structured in 36 ECTS. The 36 ECTS must be completed from the following offered tracks:

  • Deep Dive in Specific Aspects of Data Science
  • Applications of Data Science for Specific Domains
  • Innovation and Research

The deep dive in specific aspects of Data Science track deepens in advanced aspects of data management and data analysis. The applications of Data Science for specific domains track focuses on Data Science techniques specific for popular domains of application, which require specific pre-processing, management and analysis of specific data. The deep dive track is meant to get specialized in advanced techniques, while the applications track is meant to get specialized in specific domains. Finally, the Innovation and Research track delves into the connection of Data Science with business innovation and research. Courses in the innovation and research track focuses on fostering the required traversal skills to meet the high level of innovation necessary in the professional field of Data Science.

Students can choose courses from the abovementioned tracks to fulfill the elective training. However, the following maximum of ECTS per track is set: 

  • 24 ECTS on the applications of Data Science for specific domains track,
  • 15 ECTS on the innovation and research track,
  • 36 ECTS on the Deep Dive in specific aspects of Data Science (no limit).

Accordingly, students must take at least 1 elective course from the deep dive track.

Deep Dive in Specific Aspects of Data Science

Advanced Statistical Modeling
(ASM - 6 ECTS)

Algorithms for Data Mining
(ADM - 6 ECTS)

Optimization Techniques for Data Mining

Advanced Machine Learning
(AML - 6 ECTS)

Advanced Multivariate Modeling
(AMM - 6 ECTS)

Information Retrieval and Recommender Systems

Complex and Social Networks
(CSN - 6 ECTS)

Data Analysis and Knowledge Discovery

Applications of Data Science for Specific Domain

Bioinformatics and Statistical Genetics
(BSG - 6 ECTS)

Advanced Human Language Technologies

Human Language Engineering
(HLE - 5 ECTS)

Data Management for Transportation
(DMT - 4 ECTS)

Innovation and Research

Viability of Business Projects
(VBP - 6 ECTS)

Debates on Ethics of Data Science

Interdisciplinary Innovation Project
(I2P- 6 ECTS)

Techniques and Methodology of Innovation and Research in Informatics