Master in
Fundamental Principles
of Data Science

Introduction
Objectives
Admission
Frequently Asked Questions
News First application round runs from February 2 to February 27, 2026.

Master in Fundamental Principles of Data Science

An official 60 ECTS MSc · Faculty of Mathematics and Computer Science · UB

Massive amounts of data are generated every day across personal and professional life — from e-commerce to research and finance. Without analysis and interpretation, this data has no value. Data science is the new professional field that gives data meaning, sitting at the intersection of mathematics and computer science.

The Master's degree in Fundamental Principles of Data Science provides — through theoretical and practical training — the algorithmic and mathematical foundations for accurate data modeling, along with the professional competencies needed to lead data-driven projects. Special focus is placed on understanding the principles behind every algorithm: students develop the skills to modify existing algorithms and create new ones tailored to specific problems.

Topics covered in the curriculum
Computational Algebra Optimization Probabilistic Programming Machine Learning Deep Learning Complex Networks Recommender Systems Natural Language Processing Time Series Image Processing Big Data Infrastructure
Key information
Credits
60 ECTSOfficial MSc title
Places offered
30 studentsper academic year
Mode
Face to facePl. Universitat, Barcelona
Fees
€27 / credit€82 for non-EU residents
Language
EnglishB2 certificate required
Applications
Feb 2 – Feb 27, 2026First round
Coordinator
Jordi Vitrià i Marca
Faculty of Mathematics and Computer Science
Information
For any questions about the program
Visit the official program website full curriculum, faculty, and application details

Program Objectives

Tools, knowledge and competencies to work effectively as a data scientist

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

Key competencies you will develop

Understand

Grasp the principles behind every data science algorithm

Modify

Adapt existing algorithms to specific problem needs

Create

Design new algorithms from mathematical foundations

Lead

Deliver effective data-driven projects from idea to impact

Data Science visualization
Core subjects
01
Numerical Linear Algebra
02
Optimization
03
Bayesian Statistics & Probabilistic Programming
04
Machine Learning
05
Agile Data Science
06
Presentation & Data Visualization
07
Ethical Data Science
Optional subjects
Big Data Deep Learning Recommenders Probabilistic Graphical Models Business Analytics Natural Language Processing Computer Vision Complex Networks Data Science for Health Time Series

Admission & Application

Profile, requirements and how to enrol

Recommended applicant profile

The ideal applicant holds a bachelor's degree in computer science, mathematics, physics, statistics or a similar background, has a strong academic CV, and a clear interest in the field of data science.

Computer Science Mathematics Physics Statistics Similar backgrounds
Career paths

Corporate sector

Lead data initiatives in private companies and finance

Government bodies

Drive analytics in public administration and policy

Research career

Pursue academic or industrial research in data analysis

Prerequisites: regardless of your previous studies, you should have basic knowledge of programming, calculus, algebra and statistics.

Basic admission requirements
1
Spain

Official Spanish degree

An official bachelor's degree issued by a Spanish university.

2
EHEA

EHEA degree

A degree issued by an institution within the European Higher Education Area that authorises the holder to access master's degree courses in the country of issue.

3
Non-EHEA

International degree

A qualification from outside the EHEA framework, either recognised as equivalent to a Spanish degree or verified by UB as authorising master's level access in the country of issue.

Specific admission requirements

Holders of bachelor's degrees in Computer Science, Mathematics, Physics, Statistics or related qualifications.

Holders of bachelor's degrees in other subjects of similar background, subject to the authorisation of the Master's Committee.

English proficiency · B2 minimum The master's degree is taught entirely in English. Applicants must certify a B2 level or equivalent.
Enrollment
Submit your pre-enrollment application Apply through the official UB portal — first round opens February 2, 2026

Frequently Asked Questions

Everything you need to know before applying

Admission & Application

The course starts in early September and ends in July.

Yes. If you are in your final year of undergraduate studies, we accept your application to the program. If accepted, admission is conditional on finishing your undergraduate program by mid-July.

The first stage is pre-enrolment. The Master's degree committee will then conduct its selection process.

Applicants need one of the following:

(i) An official Spanish university degree.
(ii) A university degree issued by an institution regulated by the European Higher Education Area (EHEA) which entitles the applicant to enrol on an equivalent master's degree in any other EHEA-regulated institution.
(iii) A university degree issued outside the EHEA — in this case applicants must obtain either prior validation of their degree certificate or the UB's official written recognition that their degree qualifies them for EHEA university master's degree studies.

Successful admission does not qualify as recognition or validation of previous degrees. Definitive admission depends on the evaluation criteria set out by the University and the master's degree committee.

Applicants should hold an EHEA bachelor's degree in computer science or mathematics (or equivalent), a good academic record, and a clear interest in data science.

The goal of applicants should be to pursue a professional career in data science (corporate sector, public administration, finance, biomedicine, ICT, etc.) or to start a research career in data analysis. Regardless of the applicant's bachelor's degree, knowledge of programming, calculus, algebra, and statistics is required.

Since the course is taught in English, a sufficient level of English comprehension is also required.

You can enrol in one of two ways:

(i) By having your degree officially accredited and recognised as equivalent to its Spanish counterpart.
(ii) By authorisation from the dean of the faculty offering the course, confirming that your academic studies correspond to the required Spanish level and that your qualification entitles you to master's level study in the country of issue.

All supporting documents must be translated into Catalan, Spanish, or English.

Language Requirements

The master's degree course is taught entirely in English.

Any English certificate equivalent to B2 is valid. Check the equivalences on the CEFR reference page.

If you need a last-minute certificate, you can contact EIM UB Accreditations.

Fees, Credits & Financial Aid

As an indication, fees for academic year 2026–2027 were €27 per credit (€82 for students who are not EU nationals and do not currently reside in Spain).

All fees are officially regulated by the Generalitat de Catalunya, supported by agreements made by the UB's Governing Council and Board of Trustees.

ECTS (European Credit Transfer and Accumulation System) credits are the academic units used by the program to evaluate student qualifications across lectures, practical classes, hours of study outside class, seminars, assignments, project preparation, and examinations.

Check the available scholarships on the UB Grants page. For other questions about grants or financial support, contact beca.estudis@ub.edu.

Study Mode & Itineraries

Yes. The course can be done on a part-time basis. The minimum is 30 credits per year, with an estimated workload of 18 hours per week.

You have to apply to both master's degree courses. If you are accepted on both, you will be eligible for the double master's degree.

Note that the two Final Master Projects (FMP) must be separate documents. Two options are possible:

(i) Two totally independent projects, each defended in the respective committee.
(ii) Projects that share a common part plus differentiated specific chapters corresponding to each master. In this case, the FMP coordinators of both masters may form a unique committee that elaborates two different and independent resolutions.

Yes — both full-time (1 year) and part-time (2 years) itineraries are recommended:

1st Semester 30 ECTS
Computational Linear Algebra 572661 6 ECTS · Compulsory
Machine Learning 572664 6 ECTS · Compulsory
Agile Data Science 572665 6 ECTS · Compulsory
Optimization 572662 6 ECTS · Compulsory
Presentation and Visualization 572666 3 ECTS · Compulsory
Deep Learning 572669 3 ECTS · Optional
2nd Semester 30 ECTS
Bayesian Statistics & Probabilistic Programming 572184 3 ECTS · Compulsory
Ethical Data Science 574185 3 ECTS · Compulsory
Master Thesis Project 572677 12 ECTS · Compulsory
+ 4 optional courses to choose from:
Time Series Analysis 5726763 ECTS
Complex Network Analysis 5726753 ECTS
Business Analytics 5726723 ECTS
Big Data 5726673 ECTS
Data Science for Health 5741863 ECTS
Probabilistic Graphical Models 5726713 ECTS
Natural Language Processing 5726733 ECTS
Recommenders 5726703 ECTS
Computer Vision 5726743 ECTS
1st Semester 15 ECTS
Machine Learning 5726646 ECTS · Compulsory
Agile Data Science 5726656 ECTS · Compulsory
Deep Learning 5726693 ECTS · Optional
2nd Semester 15 ECTS
Bayesian Statistics & Probabilistic Programming 5721843 ECTS · Compulsory
Ethical Data Science 5741853 ECTS · Compulsory
+ 3 optional courses (3 ECTS each)
3rd Semester 15 ECTS
Computational Linear Algebra 5726616 ECTS · Compulsory
Optimization 5726626 ECTS · Compulsory
Presentation and Visualization 5726663 ECTS · Compulsory
4th Semester 15 ECTS
Master Thesis Project 57267712 ECTS · Compulsory
+ 1 optional course (3 ECTS)
Key Details
Language:
English
Location:
Pl. Universitat
Requirements:
Python, B2 Certificate
Applications:
1st round: February 3rd to February 28th, 2025
More Info
Key Details
Language:
English
Location:
Pl. Universitat
Requirements:
Python, B2 Certificate
Applications:
1st round: February 3rd to February 28th, 2025
More Info

News First application round runs from February 2 to February 27, 2026.

Master in Fundamental Principles of Data Science

An official 60 ECTS MSc · Faculty of Mathematics and Computer Science · UB

Massive amounts of data are generated every day across personal and professional life — from e-commerce to research and finance. Without analysis and interpretation, this data has no value. Data science is the new professional field that gives data meaning, sitting at the intersection of mathematics and computer science.

The Master's degree in Fundamental Principles of Data Science provides — through theoretical and practical training — the algorithmic and mathematical foundations for accurate data modeling, along with the professional competencies needed to lead data-driven projects. Special focus is placed on understanding the principles behind every algorithm: students develop the skills to modify existing algorithms and create new ones tailored to specific problems.

Topics covered in the curriculum
Computational Algebra Optimization Probabilistic Programming Machine Learning Deep Learning Complex Networks Recommender Systems Natural Language Processing Time Series Image Processing Big Data Infrastructure
Key information
Credits
60 ECTSOfficial MSc title
Places offered
30 studentsper academic year
Mode
Face to facePl. Universitat, Barcelona
Fees
€27 / credit€82 for non-EU residents
Language
EnglishB2 certificate required
Applications
Feb 2 – Feb 27, 2026First round
Coordinator
Jordi Vitrià i Marca
Faculty of Mathematics and Computer Science
Information
For any questions about the program
Visit the official program website full curriculum, faculty, and application details

Program Objectives

Tools, knowledge and competencies to work effectively as a data scientist

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

Key competencies you will develop

Understand

Grasp the principles behind every data science algorithm

Modify

Adapt existing algorithms to specific problem needs

Create

Design new algorithms from mathematical foundations

Lead

Deliver effective data-driven projects from idea to impact

Data Science visualization
Core subjects
01
Numerical Linear Algebra
02
Optimization
03
Bayesian Statistics & Probabilistic Programming
04
Machine Learning
05
Agile Data Science
06
Presentation & Data Visualization
07
Ethical Data Science
Optional subjects
Big Data Deep Learning Recommenders Probabilistic Graphical Models Business Analytics Natural Language Processing Computer Vision Complex Networks Data Science for Health Time Series

Admission & Application

Profile, requirements and how to enrol

Recommended applicant profile

The ideal applicant holds a bachelor's degree in computer science, mathematics, physics, statistics or a similar background, has a strong academic CV, and a clear interest in the field of data science.

Computer Science Mathematics Physics Statistics Similar backgrounds
Career paths

Corporate sector

Lead data initiatives in private companies and finance

Government bodies

Drive analytics in public administration and policy

Research career

Pursue academic or industrial research in data analysis

Prerequisites: regardless of your previous studies, you should have basic knowledge of programming, calculus, algebra and statistics.

Basic admission requirements
1
Spain

Official Spanish degree

An official bachelor's degree issued by a Spanish university.

2
EHEA

EHEA degree

A degree issued by an institution within the European Higher Education Area that authorises the holder to access master's degree courses in the country of issue.

3
Non-EHEA

International degree

A qualification from outside the EHEA framework, either recognised as equivalent to a Spanish degree or verified by UB as authorising master's level access in the country of issue.

Specific admission requirements

Holders of bachelor's degrees in Computer Science, Mathematics, Physics, Statistics or related qualifications.

Holders of bachelor's degrees in other subjects of similar background, subject to the authorisation of the Master's Committee.

English proficiency · B2 minimum The master's degree is taught entirely in English. Applicants must certify a B2 level or equivalent.
Enrollment
Submit your pre-enrollment application Apply through the official UB portal — first round opens February 2, 2026

Frequently Asked Questions

Everything you need to know before applying

Admission & Application

The course starts in early September and ends in July.

Yes. If you are in your final year of undergraduate studies, we accept your application to the program. If accepted, admission is conditional on finishing your undergraduate program by mid-July.

The first stage is pre-enrolment. The Master's degree committee will then conduct its selection process.

Applicants need one of the following:

(i) An official Spanish university degree.
(ii) A university degree issued by an institution regulated by the European Higher Education Area (EHEA) which entitles the applicant to enrol on an equivalent master's degree in any other EHEA-regulated institution.
(iii) A university degree issued outside the EHEA — in this case applicants must obtain either prior validation of their degree certificate or the UB's official written recognition that their degree qualifies them for EHEA university master's degree studies.

Successful admission does not qualify as recognition or validation of previous degrees. Definitive admission depends on the evaluation criteria set out by the University and the master's degree committee.

Applicants should hold an EHEA bachelor's degree in computer science or mathematics (or equivalent), a good academic record, and a clear interest in data science.

The goal of applicants should be to pursue a professional career in data science (corporate sector, public administration, finance, biomedicine, ICT, etc.) or to start a research career in data analysis. Regardless of the applicant's bachelor's degree, knowledge of programming, calculus, algebra, and statistics is required.

Since the course is taught in English, a sufficient level of English comprehension is also required.

You can enrol in one of two ways:

(i) By having your degree officially accredited and recognised as equivalent to its Spanish counterpart.
(ii) By authorisation from the dean of the faculty offering the course, confirming that your academic studies correspond to the required Spanish level and that your qualification entitles you to master's level study in the country of issue.

All supporting documents must be translated into Catalan, Spanish, or English.

Language Requirements

The master's degree course is taught entirely in English.

Any English certificate equivalent to B2 is valid. Check the equivalences on the CEFR reference page.

If you need a last-minute certificate, you can contact EIM UB Accreditations.

Fees, Credits & Financial Aid

As an indication, fees for academic year 2026–2027 were €27 per credit (€82 for students who are not EU nationals and do not currently reside in Spain).

All fees are officially regulated by the Generalitat de Catalunya, supported by agreements made by the UB's Governing Council and Board of Trustees.

ECTS (European Credit Transfer and Accumulation System) credits are the academic units used by the program to evaluate student qualifications across lectures, practical classes, hours of study outside class, seminars, assignments, project preparation, and examinations.

Check the available scholarships on the UB Grants page. For other questions about grants or financial support, contact beca.estudis@ub.edu.

Study Mode & Itineraries

Yes. The course can be done on a part-time basis. The minimum is 30 credits per year, with an estimated workload of 18 hours per week.

You have to apply to both master's degree courses. If you are accepted on both, you will be eligible for the double master's degree.

Note that the two Final Master Projects (FMP) must be separate documents. Two options are possible:

(i) Two totally independent projects, each defended in the respective committee.
(ii) Projects that share a common part plus differentiated specific chapters corresponding to each master. In this case, the FMP coordinators of both masters may form a unique committee that elaborates two different and independent resolutions.

Yes — both full-time (1 year) and part-time (2 years) itineraries are recommended:

1st Semester 30 ECTS
Computational Linear Algebra 572661 6 ECTS · Compulsory
Machine Learning 572664 6 ECTS · Compulsory
Agile Data Science 572665 6 ECTS · Compulsory
Optimization 572662 6 ECTS · Compulsory
Presentation and Visualization 572666 3 ECTS · Compulsory
Deep Learning 572669 3 ECTS · Optional
2nd Semester 30 ECTS
Bayesian Statistics & Probabilistic Programming 572184 3 ECTS · Compulsory
Ethical Data Science 574185 3 ECTS · Compulsory
Master Thesis Project 572677 12 ECTS · Compulsory
+ 4 optional courses to choose from:
Time Series Analysis 5726763 ECTS
Complex Network Analysis 5726753 ECTS
Business Analytics 5726723 ECTS
Big Data 5726673 ECTS
Data Science for Health 5741863 ECTS
Probabilistic Graphical Models 5726713 ECTS
Natural Language Processing 5726733 ECTS
Recommenders 5726703 ECTS
Computer Vision 5726743 ECTS
1st Semester 15 ECTS
Machine Learning 5726646 ECTS · Compulsory
Agile Data Science 5726656 ECTS · Compulsory
Deep Learning 5726693 ECTS · Optional
2nd Semester 15 ECTS
Bayesian Statistics & Probabilistic Programming 5721843 ECTS · Compulsory
Ethical Data Science 5741853 ECTS · Compulsory
+ 3 optional courses (3 ECTS each)
3rd Semester 15 ECTS
Computational Linear Algebra 5726616 ECTS · Compulsory
Optimization 5726626 ECTS · Compulsory
Presentation and Visualization 5726663 ECTS · Compulsory
4th Semester 15 ECTS
Master Thesis Project 57267712 ECTS · Compulsory
+ 1 optional course (3 ECTS)