Program director


Christian Fons-Rosen

UPF and Barcelona GSE

Omiros Papaspiliopoulos

ICREA-UPF and Barcelona GSE

The digital revolution brings with it an explosion of data that carries significant potential value for businesses, science, and society.

As data becomes easily available as never before, so too does its volume grow, and extracting useful quantitative insights becomes more and more challenging. 

The Barcelona GSE Master in Data Science prepares its graduates to design and build data-driven systems for decision-making in the private or public sector, offering a thorough training in predictive, descriptive, and prescriptive analytics.

The curriculum will guide students from modeling and theory to computational practice and cutting edge tools, teaching skills that are in growing global demand. 

Data Science students will be armed with a solid knowledge of statistical and machine learning methods, optimization and computing, and the ability to spot, assess, and seize the opportunity of data-driven value creation.

Students will learn how to apply classroom examples using real data and answering concrete business questions from the perspectives of different industries. Through an independent master's project and the opportunity for industrial practicum work conducted with local businesses, students can have the opportunity to solve actual analytics problems hands-on.

Our courses are taught by leading academics and researchers in the fields of Economics, Operations, and Statistics, as well as experienced professionals from the analytics industry.

The program also invites guest speakers and entrepreneurs working at the frontiers of the Data Science.

Program schedule: 

The Data Science program is organized around four pillars:

  • Statistics and Machine Learning
  • Optimization and Operational Research
  • Data Warehousing, Business Intelligence, and Big Data Analytics
  • Economics, Finance, and Policy-Making

In September, students are required to take three brush-up courses. Students who can provide evidence of sufficient past coursework may be exempt:

Course offer is subject to change.

Fall Term (September - December)

Course Title Credits Professor(s)
All courses are mandatory.
Statistical Modelling and Inference 6 Omiros Papaspiliopoulos
Deterministic Models and Optimization 6 Marc Noy
Piotr Zwiernik
Data Warehousing and Business Intelligence 3 Guglielmo Bartolozzi
Computing Lab 3 Christian Brownlees
Economic Methods for Data Science 3 Christian Fons-Rosen
Christian Michel
Jordi Quoidbach

Winter Term (January - March)

Course Title Credits Professor(s)
Machine Learning 6 Gábor Lugosi
Computational Machine Learning 3 Alexandros Karatzoglou
Financial Econometrics 6 Christian Brownlees
Electives - Select 0, 3, or 6 credits:
Stochastic Models and Optimization 3 Mihalis Markakis
Data Visualization 3 Michael Greenacre
Ioannis Arapakis
Pricing Financial Derivatives 6 Eulàlia Nualart
Quantitative Methods of Market Regulation 3 Albert Banal
Quantitative and Statistical Methods II 6 Joan Llull
Workshops (0 credits)
Workshop on Deep Learning
I​​n this seminar we are going to present the main ideas and concepts behind Deep Learning. We will motivate the use of deep learning methods in machine learning and introduce three deep network architectures that are extensively used in practice namely: feedforward networks, convolutional networks and recurrent networks.
0 Alexandros Karatzoglou

Spring Term (April - June)

Course Title Credits Professor(s)
Master project 6 Coordinator: Christian Fons-Rosen
Electives - Select 0, 3, or 6 credits:
Note: This list is not exhaustive. Other electives will be available.
Industrial Practicum
Complements the master project with credits if student is placed in company working full-time
6 Subject to availability and selection process.
Coordinator: Christian Fons-Rosen
Topics in Big Data Analytics I 3 Pau Agulló
Topics in Big Data Analytics II 3 TBD
Social and Economic Networks 6 Joan de Martí
Text Mining for Social Sciences 3 Stephen Hansen
Machine Learning for Finance 3 Argimiro Arratia
Digital Market Design 3 Sandro Shelegia
Policy Lessons **  3 Albert Bravo-Biosca
Juan Francisco Jimeno
Workshops (0 credits)
Workshop on Distributed Machine Learning
I​​n this workshop we will examine the basic concepts (RDDs, transformations, runtime architecture) behind distributed Machine Learning (ML) using Spark, a fast and general-purpose cluster computing platform. We will motivate the use of distributed ML and introduce MLlib, a library of machine learning functions. MLlib has been designed to run in parallel on clusters, contains a variety of learning algorithms and is accessible from all of Spark’s programming languages (e.g., Java, Python).

During the lab session we will try to implement some basic supervised/unsupervised algorithms introduced in the lecture. We will aim to be using python and some additional useful libraries, such as NumPy and Pandas.

0 Ioannis Arapakis

** Requires some prerequisite knowledge and authorization of the program director. Availability depending on sufficient demand.

Master degree awarded

Upon successful completion of the program, students will receive a Master Degree in Data Science awarded jointly by Universitat Autònoma de Barcelona (UAB) and Universitat Pompeu Fabra (UPF).

All Barcelona GSE master degrees have been recognized by the Catalan and Spanish Education authorities within the framework of the Bologna Process (in Spanish, “Master Universitario o Master Oficial”). Indicadors de qualitat

What skills and knowledge will I acquire in this program?
  • Expertise in modern computational, high-dimensional and simulation-based Statistics and Machine Learning
  • Solid evaluation of the opportunities of data-driven value creation within companies and organizations
  • Ability to apply appropriate statistical methodologies and optimization techniques in solving complex problems
  • Innovation with database management systems and distributed processing in a cloud computing environment
  • Experience analyzing Big Data from the Internet of Things (industrial sensor data), the Internet of People (social and location data), and business transaction data
  • Effective communication of data analysis results through presentation and aesthetic charting skills
Who will benefit from this program?
  • Graduates in Economics or Business with a solid economics grounding, and keen interest in quantitative methods
  • Graduates in Computer Science, Engineering, Mathematics, Physics, or Statistics with the ambition to work on real-world problems and data
  • Programming professionals looking to acquire analytical and quantitative tools to leverage their experience
  • Aspiring PhD students looking for rigorous training in quantitative and analytical methods
Industrial Practicum

The industrial practicum is an in-depth analytics project that takes place during the final three months of the master. The practicum gives Data Science students the opportunity to complete a "deep dive" analytics project using real data from selected companies around Barcelona.

Students will dedicate 300 hours to the practicum, collaborating with companies on-site using real data to solve a specific challenge that requires the types of "skill bundles" they have acquired during the first two terms of the master program.

Companies that have expressed interest in offering practicum projects to Barcelona GSE students include commercial banks, consulting firms, mobile application developers, risk analysts, and online retail platforms, among others. With a wide menu of companies competing for their attention, students are very likely to be matched with a practicum in the industry where they might prefer to work after graduation.

In 2016, 16 companies were selected for the industrial practicum. One or more Barcelona GSE Data Science students completed a practicum with each of the following firms:

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Who hires Data Science graduates?
  • Consumer Goods, E-commerce, Entertainment, Pharmaceutical, and Telecommunications Industries
  • Logistics and Transportation Industries
  • Finance and Insurance Industry
  • Consulting and Research Organizations
  • Banking and Public Institutions

Examples of placements from the first class of Data Science graduates (Class of 2015):

  • Accenture
  • Bluecap Management Consulting
  • Kernel Analytics
  • King
  • Schibsted Media Group
  • Vueling

Download results for Data Science 2015

Data Science activities at the Barcelona GSE

Data Science Visiting Fellows

Barcelona GSE Data Science visiting fellows are distinguished researchers working in academia or the private sector who are hosted by the Barcelona GSE to engage in research projects with the BGSE Data Science team and contribute to training activities.

Visiting Fellows 2016-17:

photoIoannis Kosmidis
Senior Lecturer, University College London
Professor Kosmidis will give a workshop on statistical learning of generalized linear models with massive amounts of data. Additionally, he will make a presentation on sports analytics.


Blog by Data Science students

Barcelona GSE Data Scientists is a blog written by our students about program activities and current topics in Data Science.

Here are some of the recent posts:

Read more on the BGSE Data Science Blog

Data Science Summer School

The main aim of the Data Science Summer School is to introduce participants to some of the tools and methods of Data Science.

Visit Summer School page




Data Science Student Profile 2016-17

25 students from 14 countries (76% of students come from outside Spain). While the most common undergraduate background among them is Economics or Finance, a variety of others such as Physics and Psychology reveals another dimension of diversity to the class.

  • Personally, I think the Data Science Program at the BGSE is probably the best developed Data Science program in Europe. My first impressions are amazing. The people who teach are the best at what they do, they are so motivated and are able to motivate us, and I'm really happy with that. I expect to be trained at a really high level to be able to compete with other people in academia as well as industry.
  • I chose Barcelona GSE for my masters because I think it is a perfect combination between strong theoretical lectures and an open gate to the business world. By studying here, I can make contacts with firms in Barcelona. The people from my masters are super friendly. They are amazing, brilliant people who come from many different backgrounds. I find this very enriching and I think I can learn lots from them.
  • I think the Data Science Program perfectly balances theory and practice, thus not just equipping us with a thorough theoretical background in statistics, machine learning, and optimization, but also trains us on how to apply analytics in complex real-word problems, both in industry and academia. Classes are small with people coming from many different backgrounds and experiences. Overall, I feel very happy with the quality, the focus, and the depth of the program.


Data Science alumni career paths

  • Most common job titles: Data Analyst, Data Scientist, Consultant
  • Companies who hired graduates include: Accenture, Kernel Analytics, King, Schibsted, and Vueling, among others
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    Sílvia Ariza '15

    Product Manager - Big Data Intelligence
    Right after graduating from the Master's in Data Science I had a wide variety of job offers as a data scientist. The data sector is growing quickly, and many companies in Barcelona are starting to realize how important it is to have a Big Data department. It was very encouraging to see that my efforts were so recognized.
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    Laura Cozma '15

    Attending the Data Science master courses gave me the right mix of skills that any employer would value. Not long after graduating, I found myself with 5 very interesting job offers to choose from, any one of which would have given me the opportunity to dig deeper into machine learning, text mining, data visualization, and the statistical techniques I studied at the BGSE. If I had the chance, I would do it all over again!
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    Rishabh Agnihotri '15

    PhD Student
    Being part of the first batch of the Data Science masters was a firm yet fun foray into the magic that math and programming can perform! The focus of this program was to nurture mathematical, probabilistic and statistical maturity, followed by a series of applied projects in different courses. This rigorous approach equipped us with the tools that ensured that we graduated at the top percentile of the data scientist work force. At the same time, it provided us with a more research oriented mindset that data science demands from its practitioners. Indeed a beautiful, once-in-a-lifetime experience that prepared us for our careers while blessing us with excellent friends!