Master in Data Science

Note: This is a new master that replaces the MIRI - Data Science



The master degree in Data Science equips graduates with solid foundations and hands-on experience in fundamental aspects of data management and analysis to extract hidden knowledge from structured and unstructured big data and in building adaptive analytical systems that are able to exploit that knowledge in modern organizations. In particular, graduate students will be prepared to address the new challenges of the so-called data-driven society and develop systems based on data to tackle relevant topics such as fraud detection, bioinformatics and e-health, information extraction from highly unstructured data, real time analysis of sensor data and social networks, customer relationship management, etc. 
The master degree in Data Science has been designed with the objective of training highly qualified professionals who will be provided with fundamental understanding and the required competences (that is, the combination of knowledge and skills) to apply their learning in the demanding field of Data Science. Therefore, this program will generate highly innovative, interdisciplinary professionals with a strong research-oriented perspective, specially prepared to face the challenges identified by industry.


The master’s degree in Data Science is an interdisciplinary programme that bridges Computer Science and Mathematics. The recommended entry profiles are the following:

  • Computer Science and equivalent degrees.
  • Mathematics and equivalent degrees.
  • Classic Engineering degrees or equivalent degrees with a strong mathematical background covering in depth algebra, calculus, statistics and with at least a minor in Computer Science.

Other profiles might be considered provided they guarantee solid foundations in Mathematics and Computer Science as to enable the candidate to follow the programme. 
The master's degree in Data Science is offered to students from all over the world. Candidates must have a good comprehension, oral and written expression in English. Candidates must also be motivated to discover knowledge from data in the fascinating world of sciences and technologies. 




Four semesters. (120 ECTS)



  • Full-time: 20h lectures, 20h personal work per week
  • Part-time: half full-time workload



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




The admission period to start the Master programme in September 2021, is open from 25 February to 5 June, 2021.

The admission period is divided into two periods:

  • 1st period. From 25 February to 12 March. The decision on applications will be announced no later than 19 March.
  • 2nd period. From 15 March to 4 June. The decision on applications will be announced no later than 11 June.
The decision may be that the candidate has been admitted, or that the candidate has been rejected or, in the case of the 1st period, that the decision has been postponed until the following period.

The notification from 11 June will include a waiting list with all the candidates whose position in the candidates list is higher than the number of places offered in the Master. The list will be ordered by candidates access mark. The candidates in that list can become admitted if some of the candidates firstly admitted decide to give up their seat.

Admitted candidates must accept their admission and confirm it with the payment of the allocation fee before 17 Juny, 2021. After that day, we will understand that they give up their seat, so their admission will be revoked and the place assigned to the following candidate in the waiting list.

The official list of accepted candidates will be published on 18 June, 2021, on the FIB website, in the "Enrollment" section of each Master programme.

Fees and payment options

Information about prices and fees for the UPC's official master's degrees can be found at Fees and payment options.

Our masters have specific grants and scholarships.



The admission requirements for the UPC's official masters can be found at What are the requirements to enroll in a master's degree?


Candidates must provide proof of their English proficiency, with at least a B2 level of the Common European Framework of Reference for Languages (or equivalent).

Required Documents

  1. Curriculum Vitae
  2. DNI, NIE or passport
  3. Academic Personal Certificate. The certificate must detail the number of hours and credits studied and must include the grade awarded for each subject. This certificate also must contain information on the mark scheme and how grades are awarded.
  4. Diploma (or similar document). This document must confirm that the duration of the degree was at least three years and that is it a valid precursor to studies at postgraduate level (master’s degree) in that country. If that is not possible, it must indicate the level of higher education that the degree gives access to in the country where it was awarded.
  5. English knowledge Certificate (minimum: B2 of Common European Framework of Reference for Languages)
  6. If the applicant holds other qualifications related to the subject area of the Master’s Degree course applied for and that are different from those given to meet entrance requirements, it is necessary to include the original certificates and copy (or official copy) with the application.

IMPORTANT: All documents issued outside Spain or in non-Spanish-speaking countries must be translated into Spanish or Catalan. Documents issued in countries that do not belong to the European Higher Education Area must be stamped and legalised by the appropriate government department.

Selection Criteria

The Academic Committee is in charge of the admission of the candidates. The criteria are: Academic Transcripts (60%), Relevance of the Bachelor (20%), Background and professional experience (5%) and Motivation Letter (15%).

Expected Applicant Profile

For a successful development of the studies leading to the title of Master's Degree in Data Science taught at UPC, the admission profile must correspond to the following personal and academic characteristics:

Technical Competences:

  • Knowledge in algorithmics, data structures, programming and databases equivalent to, at least, that of a degree with a minor in Computer Science.
  • Knowledge in algebra, calculus and statistics equivalent to the fundamental knowledge obtained in Computer Science, or in the first years of the main degrees of Engineering.
  • Comprehension, oral and written expression in English (B2 level or equivalent).


  • Aptitude for study and organize your learning.
  • Advanced skills for logical reasoning and problem solving.


  • Ability to analyse and synthesise information.
  • Ability to argue, reason and express ideas.


  • Organised, curious, enterprising person willing to apply knowledge to real situations.
  • Creative and innovative capacity in front of the evolution of technological advances.
  • Interest in Information and Communication Technologies.
Recommended Bachelor Degrees

Since Data Science sits in the confluence of Computer Science and Mathematics, the main recommended entry profiles are:

  • Students with a degree in Computer Science.
  • Students with a degree in Mathematics.

However, there are several other degrees that would allow to successfully pursue a master's degree in Data Science. Specifically, any degree that guarantees a solid knowledge in the Computer Science and Mathematical technical competences previously mentioned. For reference, the following degrees typically meet the expected technical competences:

  • Students with a degree in Physics or equivalent.
  • Students with a degree in Statistics or equivalent.
  • Students with a degree in Telecommunication Sciences and Technologies, Telecommunication Technology and Services Engineering, Electronic Telecommunication Engineering or equivalent.
  • Students with a degree in Civil Engineering or equivalent.
  • Students with a degree in Engineering in Industrial Technologies, Industrial Electronics and Automation or equivalent.

Given the diversity of degrees in these areas, and since this master's degree does not consider training complements, the academic committee will check the bachelor syllabus of the applicant and assess whether the study plan followed adequately covers the Mathematics and Computer Science technical skills required in order to be admitted.

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