Master's degree in Data Science

Barcelona School of Informatics (FIB)

The master’s degree in Data Science aims to create a benchmark for excellence in the field of data science. This eminently interdisciplinary degree is based on two distinct pillars that are equally essential to data science: data management and data analytics. The degree thus provides a holistic view of the field that also covers cross-disciplinary topics such as ethics and entrepreneurship. The degree aims to produce highly qualified professionals who have a strong ability to innovate in data management and data analytics. Graduates with these skills are in high demand in both the academic sector and industry. The master’s degree also seeks to generate synergies and to foster the exchange of information and experiences in order to nurture the education, research and innovation triangle, which is vitally important in data science.

General details

Duration and start date: 2 academic years, 120 ECTS credits. Starting September
Timetable and delivery: Face-to-face
Fees and grants: Approximate fees for the master’s degree, excluding other costs (does not include non-teaching academic fees and issuing of the degree certificate):
€3,320 (€12,662 for non-EU residents).
More information about fees and payment options
More information about grants and loans
Language of instruction: English
Information on language use in the classroom and students’ language rights.
Location: Barcelona School of Informatics (FIB)
Official degree: Recorded in the Ministry of Education's degree register

Admission

General requirements

Academic requirements for admission to master's degrees

Specific requirements

Since this master’s degree is taught entirely in English, applicants must certify a B2 level (CEFR) of English (or equivalent).

Direct admission

The recommended entrance qualifications for the master’s degree are the following:

Bachelor’s or pre-EHEA degree in Informatics Engineering
Bachelor’s or pre-EHEA degree in Mathematics

The following qualifications are considered related as they ensure a sound knowledge of informatics and mathematics:

Bachelor’s degree in Physics (or equivalent)
Bachelor’s degree in Statistics (or equivalent)
Bachelor’s degree in Telecommunications Science and Technology, Telecommunications Technologies and Services Engineering or Electronic Engineering and Telecommunications (or equivalent)
Bachelor’s degree in Civil Engineering (or equivalent)
Bachelor’s degree in Industrial Technology Engineering or Industrial Electronics and Automatic Control Engineering (or equivalent)

Holders of recently introduced degrees that are closely related to data science will be considered. Such degrees include:

Bachelor’s degree in Bioinformatics (or equivalent)
Bachelor’s degree in Artificial Intelligence (or equivalent)
Bachelor’s degree in Data Science and Engineering (or equivalent)

In the case of related degrees, priority will be given to students who have followed a curriculum that ensures a thorough knowledge of mathematics and informatics. In addition, given the highly international character of the master’s degrees offered by the Barcelona School of Informatics (FIB) and this master’s degree in particular, degrees held by foreign students that are equivalent to those listed above will be considered.

Places

Pre-enrolment

Pre-enrolment closed (consult the new pre-enrolment periods in the academic calendar).
How to pre-enrol

Enrolment

How to enrol

Legalisation of foreign documents

All documents issued in non-EU countries must be legalised and bear the corresponding apostille.

Curriculum

First semester

Second semester

Advanced Statistical Modelling 6
Algorithmics for Data Mining 6
Big Data Management 6
Bioinformatics and Statistical Genetics 6
Cloud Computing and Big Data Analytics 6
Complex and Social Networks 6
Data Management for Transportation 4
Debates on Ethics of Data Science 3
Human Language Engineering 4.5
Interdisciplinary Innovation Project 6
Introduction to Research 3
Introduction to Research 6
Machine Learning 6
Mining Unstructured Data 6
Optimization Techniques for Data Mining 6
Semantic Data Management 6
Techniques and Methodology of Innovation and Research in Informatics 6
Viability of Business Projects 6

Third semester

Fourth semester

Master's Thesis 30

CompulsoryECTS
OptionalECTS
ProjectECTS

Professional opportunities

Professional opportunities

Graduates work in data management and data analytics. The main positions related to work in these fields are the following:

Data scientist
Data engineer
Data specialist
Data administrator
Systems architect
Systems analyst
Digital transformation leader (DTL)
Chief information officer (CIO)
Chief data officer (CDO)

Competencies

Generic competencies

Generic competencies are the skills that graduates acquire regardless of the specific course or field of study. The generic competencies 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

The ability to develop efficient algorithms based on knowledge and understanding of the theory of computational complexity and the main data structures in the field of data science.
The ability to apply the basic principles of data management and processing to problems in the field of data science.
The ability to apply data integration methods to solve data science problems in heterogeneous environments.
The ability to apply scalable methods for storage and parallel processing of data, including continuous data flows, after identifying the most suitable approaches for tackling a particular data science problem.
The ability to model, design and implement complex data systems, including data visualisation.
The ability to design data science processes and to apply scientific methods to draw conclusions about populations and take decisions accordingly, based on structured or unstructured data, including data that may be stored in heterogeneous formats.
The ability to identify the limitations imposed by data quality when tackling data science problems and to apply techniques to reduce their impact.
The ability to extract information from structured and unstructured data, taking into account their multivariate nature.
The ability to apply appropriate methods for analysing other types of formats, such as processes and graphs, in the field of data science.
The ability to identify machine learning and statistical modelling methods for solving a specific data science problem and to apply them in a rigorous manner.
The ability to analyse and extract knowledge from unstructured information by applying natural language processing techniques and through the use of text and image mining.
The ability to apply data science methods to multidisciplinary projects to solve problems in new or unfamiliar domains, selecting approaches that are economically viable, socially acceptable and in accordance with current legislation.
The ability to identify the main data ethics and privacy issues affecting data science projects (in terms of both data management and data analytics) and to develop and implement appropriate measures to mitigate related threats.
The ability to carry out and present and defend before an examination committee an original, individual piece of work consisting of a comprehensive data science engineering project that synthesises the competencies acquired on the degree.

Double-degree agreements

With universities around the world

Master's degree in Data Science + Master of Science degree in Data Science (Dipartimento di Matematica “Tullio Levi-Civita”, Università degli Studi di Padova, Padova, Italy)
Master's degree in Data Science + Degre of Engineer in Informatics applied to Health (École d'Ingénieurs-ISIS, Institut National Universitaire Jean-François Champollion (INU Champollion), Albi, France)

Quality accreditation

Check the degree’s main quality indicators in the University Studies in Catalonia portal of the Catalan University Quality Assurance Agency. Find information on topics such as degree evaluation results, student satisfaction and graduate employment data.
Further information