Foundations of
Data Science

Apply for Admission
Second application period ends June 12th (5 places available)



Many aspects of our personal and professional, from online shopping to research or finance, produce great volumes of data. Without control nor interpretation data is just noise. Data science is a new professional area that pretends to give value to data through actionable analysis and interpretation. The data scientist skills lie in the intersection of mathematics and computer science. The Master in Foundations of Data Science gives the algorithmic and mathematical basis needed for the correct modeling and analysis of data by means of practical oriented sessions, as well as the professional skills for confronting data based projects. It emphasizes the skills related to the understanding of the foundations of the algorithms behind data science and to the skill of modifying and creating new specific algorithms tailored to the data project needs. The master includes topics ranging from numerical linear algebra, optimization, or probabilistic programing, to machine learning, deep learning, complex networks, and recommender techniques; and their application to natural language processing, temporal series or information extraction from images, using technologies capable of storing and processing large volumes of data, “big data”.



Monday - Friday 15.00h - 19.00h

Total study load:



46,50 euros per credit (82 euros per credit for students who are not EU nationals and do not currently reside in Spain). Fees for the academic year 2016-2017.



Estimated workload:

Full time workload: 40 hours per week (including lectures and homework).

Part time workload: from 18 hours per week (including lectures and homework).


First application period ends 28/04/2017.

- Provisional announcement of admission decisions: 05/05/2017.

- Definitive announcement of admission decisions: 19/05/2017.

Second application period (if places remain available): 19/05/2017 - 12/06/2017

Skills and competences

  • To understand the process of data valorization and its role in decision making.
  • To know how to gather and extract information from structured and non-structured data sources.
  • To know how to clean and massage data with the goal of creating valuable, manageable, and informative data sets.
  • To be able to use storage and processing technologies for handling large data sets.
  • To know how to hypothesize and develop the intuition about a data set using exploratory analysis techniques.
  • To efficiently and effectively apply machine learning analytic and predictive.
  • To understand, create, and modify analytic and exploratory algorithms operating over data.
  • To verify and quantify the validity of an hypothesis using data analytics.
  • To communicate results using appropriate communication skills and visualization tools and techniques.
  • To know the privacy and data protection legislation and the data scientist professional code and ethics.
  • To be able to use agile development methodologies for managing data science projects.

Recommended applicant profile

Ideally, applicants to this master's degree should hold an EHEA bachelor's degree in computer science, mathematics or an equivalent qualification, as well as possessing a strong academic record and a particular interest in the field of data science, with the goal of pursuing a professional career in data science in a company or public administration, or in sectors that require specialists with a high level of training in data analysis, interpretation and visualization (finance, bio-medicine, information and communication technologies, etc.) or to start a research career in topics related to data analysis. Independently of the bachelor’s degree of the applicant, knowledge on programming, calculus, algebra and statistics is required. Since the degree is taught in English, applicants should also possess a sufficient level of comprehension to be able to follow the course in this language.

Admission 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

This course is open to

  • Applicants in possession of an EHEA bachelor’s degree in Computer Science, Mathematics, Physics, Statistics or an equivalent official degree qualification (without bridging courses).
  • Applicants in possession of an EHEA bachelor’s degree in other engineering fields or an equivalent official degree qualification (with bridging courses).
  • Applicants in possession of a non-EHEA bachelor’s degree in Computer Science, Mathematics, Physics, Statistics or equivalent official degree qualifications with approval from the Coordination Committee (with bridging courses).
  • Applicants in possession of a non-EHEA bachelor’s degree in other Engineering fields or equivalent official degree qualifications with approval from the Coordination Committee (with bridging courses).

Since the degree is taught in English, applicants must be in possession of an official title equivalent to B2 level.

The Coordination Committee will select applicants according to academic records and professional experience.


Numerical Linear Algebra

Vector and matrix operations. Vector and matrix norms. SVD. Matrix Factorization. Exploiting matrix structure. Iterative methods.


Convex optimization. Duality. First and second order methods. Interior points. Stochastic optimization.

Bayesian Statisticc and Probabilistic Programming

Computational statistics. Bayesian statistics. Computational sampling techniques. Probabilistic programming.

Machine learning

Statistical learning theory. Supervised learning. Generative models. Linear models. Kernel methods. Ensemble methods. Manifold learning. Clustering.

Agile science data

Agile development for data science projects. Data infrastructures. Solution deployment. Privacy and ethics.

Presentation and data visualization

Perception and patterns theory. Data and visualization models. Creation of visualizations.

Big data *

Large scale processing frameworks. Hadoop. Spark.

Advanced databases techniques *

Real time and batch data ingestion. SQL and large scale database design. NoSQL solutions.

Deep learning *

Introduction to tensorflow. Fully connected networks. Convolutional neural networks. Recurrent neural networks. Unsupervised deep learning.

Recommenders *

Collaborative filtering. Item based recommenders. Hybrid models. Context-aware recommenders. Large scale recommenders.

Probabilistic graphical models *

Bayesian networks. Markov networks. Exact and approximate inference. PGM decision making.

Business Analytics *

Natural Language Processing *

Computer Vision *

Introduction to images. Image processing. Retrieving information from images.

Complex networks *

Models of complex networks. Micro and macro scale. The mesoscale. Network Dynamics.

(* Eligible Course)

In order to successfully finish the master’s program, students are required to complete, durng the second semester, a capstone project. Capstone projects are “experiential” projects where students take what they’ve learned throughout the master program and apply it to examine a specific idea.

Double Master Degree

You can get a Double Master Degree on Foundations of Data Science (FDS) and Advanced Mathematics (AM) by completing, in 18 months, 60 ECTS of the UB Master on Foundations of Data Science plus the 3 mandatory courses and 3 optional courses from the UB Master on Advanced Mathematics.

This 93 (or 96) ECTS can be distributed as follows:

  • First Semester: A mandatory or optional course from the MAM (6) + Optimization (6) + Bayesian Statistics and Probabilistic Programming (6) + Machine Learning (6) + Numerical Linear Algebra (6)
  • Second Semester: Presentation and Visualization (3) + Advanced Methodology in Mathematics (3) + Two optional courses from the MAM(12) + Five optional course from the MFDS (15)
  • Third Semester: A mandatory course from the MAM (6) + An optional or the other mandatory course from the MAM (6) + MFDS Capstone Project (12) or MAM Capstone Project (15) + Agile Data Science (6)


Edifici Històric de la Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007, Barcelona

get in touch

Please, contact us if you have any question.

Email Us

Looking forward to hearing from you!