Objectives and competences

Large amounts of data are generated in many aspects of personal and professional life, from electronic purchases to research or finances. If these data are not monitored or interpreted, they have no value. Data science is a new professional area that aims to give these data meaning by analysing and interpreting them. A data scientist is a new professional profile at the intersection between maths and computer science.
The master's degree in Fundamental Principles of Data Science includes aspects that cover a broad field: computational algebra, optimisation or probabilistic programming, automatic learning techniques and deep learning, complex networks, recommendation systems, applications to natural language processing, time series, extraction of information in images, and support for infrastructures that process big data."


Basic information

TypeMaster's degree
Faculty or schoolFaculty of Mathematics and Computer Science
Branch of knowledge
  • Computer and systems engineering
Mode of delivery
  • Face-to-face
Number of places available30
Length of course1 academic year
Language(s) of instructionEnglish
Approximate price per credit27.67 euros per credit (82 euros for students who are not EU nationals and do not currently reside in Spain). Fees for the academic year 2023-2024
Course detailsIndicators
Open pre-enrolmentNo
Open enrolmentNo
Lead to doctoral studiesYes
Admission for applicants not holding a degree qualificationNo
Bridging coursesYes

Objectives and competences


The Master's Degree in Fundamental Principles of Data Science aims to provide the tools, knowledge and competences required to work effectively as a data scientist. The course focuses on the competences required to understand, modify and create algorithms, analytical and exploratory methods and techniques; as well as leadership abilities and the development of effective data-based projects.


The master's degree covers basic and general competences for managing time, resources and projects, and for working in teams. In addition, it provides tools to face the challenges in the discipline with analytical, critical and creative capacity. The course will focus specifically on the following competences:

  • Capacity to understand the process of analysing data, and the role of data in decision-making.
  • Capacity to gather and extract information from structured and unstructured data sources.
  • Capacity to clean and correct data, in order to create datasets that are easy to manipulate and informative.
  • Capacity to use technologies for the storage, recovery and processing of large volumes of data.
  • Capacity to learn how to propose hypothesis and develop intuition about a dataset using exploratory analysis techniques.
  • Capacity to effectively use analytical and predictive tools for automatic learning.
  • Capacity to understand, develop and modify analytical and exploratory algorithms for a dataset.
  • Capacity to verify and quantify the validity of a hypothesis, using data analysis.
  • Capacity to communicate results using appropriate communication and display techniques.
  • Knowledge of legislation on data protection and privacy, and on the ethical code in professional practice.
  • Capacity to use effective development methods for data science projects.

Access and admission

Applicant profile and access requirements

Recommended applicant profile

The ideal applicant for this master's degree holds a bachelor's degree in computer science, mathematics or related studies, has a strong academic CV, and an interest in the field of data science. Applicants will be looking to work in data science in the corporate sector or in government bodies, and in sectors that require high-level specialists in data analysis, interpretation and visualisation (such as finance, biomedicine, information and communication technologies, etc.), or to begin a research career focused on data analysis. Regardless of their previous studies, students of this master's degree should have basic knowledge of programming and of calculus, algebra and statistics.

Access requirements and conditions

General requirements
In accordance with Article 16 of Royal Decree 1393/2007, of 29 October, students wishing to be admitted to a university master's degree must hold one of the following qualifications:

  • Official Spanish university degree.

  • A degree issued by a higher education institution within the European Higher Education Area framework that authorizes the holder to access university master's degree courses in the country of issue.

  • A qualification issued by an institution outside the framework of the European Higher Education Area. In this case, applicants must request homologation of the degree to its equivalent official Spanish university qualification or obtain express approval from the University of Barcelona, which will conduct a study of equivalence to ensure that the degree is of a comparable level to an official Spanish university qualification and that it grants access to university master's degree study in the country of issue. Admission shall not, in any case, imply that prior qualifications have been recognized as equivalent to a Spanish master's degree and does not confer recognition for any purposes other than that of admission to the master's degree course.

Specific requirements
Applicants with the following qualifications may be admitted:
  • Holders of bachelor's degrees in Computer Engineering, Mathematics, Physics, Statistics or related qualifications (no bridging courses are required).
  • Holders of bachelor's degrees in other engineering subjects or equivalent qualifications, with the authorisation of the Master's Committee (bridging courses will be required).
  • Holders of bachelor's degrees in Computer Engineering, Mathematics, Physics, Statistics or related qualifications who hold official qualifications from outside of the EHEA (bridging courses will be required).
  • Graduates in other engineering subjects or equivalent qualifications from outside the EHEA, who have official authorisation from the Master's Committee (bridging courses will be required).

Given that the master's degree is taught entirely in English, applicants must certify that they have at least level B2 English.

If applicants must take bridging courses, the Committee will propose a maximum of 30 additional credits from Computer Engineering degrees or from the Bachelor's Degree in Mathematics, depending on the applicant's previous training. The bridging courses will include the following aspects:

  • Introduction to scientific computing and numerical methods
  • Databases
  • Workshop on new uses of computers
  • Probability and statistics
  • Advanced algorithms
  • Artificial intelligence
  • Distributed software



First period (25 places)

Submission of applications: February 1, 2023, to March 1, 2023
Publication of the list of eligible/ineligible students: March 16, 2023
Publication of decision on Masters' accepted students: March 31, 2023
Web form for the master's coordinator to authorize the subjects to enroll: from June 26, 2023 to July 28, 2023
Enrolment: September 2023
Second period (5 places):

Submission of applications: May 22, 2023, to June 23, 2023
Publication of the list of eligible/ineligible students: July 7, 2023
Publication of decision on Masters' accepted students: July 17, 2023
Web form for the master's coordinator to authorize the subjects to enroll: from June 26, 2023 to July 28, 2023
Enrolment: September 2023.
* These places will be allocated among those students who were not selected after the first period as well as those students who submitted their applications during the second period.


  • Pre-enrolment fee: A pre-enrolment fee of 30,21 euros is charged. Students who apply to more than one master's degree must pay the fee for each pre-enrolment request. Pre-enrolment requests cannot be processed until this fee has been paid.Fees will only be refunded if the master's degree in question is suspended.
  • Reserved places: A maximum of 5% of the new places of the master's degree are reserved for students who meet the general and specific access requirements and accredit the recognition of a degree of disability equal to or greater than 33%.

Required documentation

  • Pre-enrolment application
  • Photocopy of degree certificate or equivalent qualification. In case of admission, foreign degrees that require so should be translated and authenticated through diplomatic channels before completing the enrolment.
  • Other specific documentation related to the selection criteria

Selection criteria

In the selection process, the following aspects will be evaluated, with the weighting indicated below:
- Academic transcript from the bachelor's or pre-EHEA degree (70%)
- Professional experience (30%)



As a general rule, at the UB you will be required to enrol online. Remember that you can lose your place if you do not enrol on the day you have been assigned

Course curriculum

Subjects and course plans

Distribution of credits

Compulsory 33
Optional 15
Compulsory placements 0
Compulsory final project 12

List of subjects

Subject Type Language Credits
Specialization: Principles of Data Science
Agile Data Science Compulsory 1st semester 6
Analysis of Complex Networks Optional 2nd semester 3
Automatic Learning Compulsory 1st semester 6
Bayesian Statistics and Probabilistic Programming Compulsory 2nd semester 3
Big Data Optional 2nd semester 3
Business Analytics Optional 2nd semester 3
Computer Vision Optional 2nd semester 3
Data Science and Health Optional 2nd semester 3
Deep Learning Optional 1st semester 3
Ethics for Data Science Compulsory 2nd semester 3
Final Project Compulsory 1st semester
2nd semester
Natural Language Processing Optional 2nd semester 3
Numerical Linear Algebra Compulsory 1st semester 6
Optimization Compulsory 1st semester 6
Presentation and Visualization Compulsory 1st semester 3
Recommenders Optional 2nd semester 3
Time Series Analysis Optional 2nd semester 3

Previous years


Placements in a company or another type of organization are an integral part of university studies, providing first-hand experience of working methodologies in students' chosen professional fields. They offer invaluable practical experience for the transition into work after graduation.

Placements are supervised by tutors and subject to assessment. They are therefore included in the academic record.  There is also an option to complete non-curricular placements of up to 500 hours, which can be extended to 900 hours. For both curricular and non-curricular placements, an educational cooperation agreement is signed between the UB and the company, institution or other organization at which the placement will be carried out.

Institutional information

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