Master's Degree in Data Science: Data Science Program

Program Director

faculty

Christian Fons-Rosen

PhD, London School of Economics

UPF and Barcelona GSE - on leave

Scientific Director

faculty

Omiros Papaspiliopoulos

PhD, Lancaster University

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.

 


logoAccenture Data Science Scholarship

Accenture sponsors a full scholarship for one student in the Barcelona GSE Data Science master program.

Scholarship criteria and details


 

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

Ioannis Arapakis
Ilias Leontiadis
Nandan Rao

Economic Methods for Data Science 3 Christian Michel

 

Winter Term (January - March)

Course Title Credits Professor(s)
Machine Learning 6 Gábor Lugosi
Computational Machine Learning 3 Alexandros Karatzoglou
Students must enroll in one section of the following course:
Macroeconomics and Finance: Financial Econometrics 6 Christian Brownlees
Macroeconomics and Finance: Pricing Financial Derivatives (Part I and Part II) 6 Eulàlia Nualart
Select from the following:
Stochastic Models and Optimization 3 Mihalis Markakis
Data Visualization 3 Michael Greenacre
Ioannis Arapakis
Quantitative Methods of Market Regulation 3 Albert Banal
Quantitative and Statistical Methods II 6 Joan Llull

Spring Term (April - June)

Course Title Credits Professor(s)
Master project 6 Coordinator: Christian Fons-Rosen
Select from the following:
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 Emiliano Carluccio
Topics in Big Data Analytics II 3 Gergely Neu
Carlos Segura
Gaston Besanson
Networks: Concepts and Algorithms 3 Joan de Martí
Networks: Models and Applications 3 Joan de Martí
Text Mining for Social Sciences 3 Hannes Mueller
Omiros Papaspiliopoulos
Machine Learning for Finance 3 Argimiro Arratia
Digital Economy 3 Massimo Motta
Juan-José Ganuza
Rosa Ferrer
Policy Lessons **  3 Albert Bravo-Biosca
Juan Francisco Jimeno
Blockchain: From First Principles to Analytics 3 Jesse Seaver
Gaston Besanson

** 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.

Some of the companies collaborating with the industrial practicum are: 

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Sample practicum project titles

  • Skill Assessment of Seasonal Temperature and Precipitation Forecast over Europe
  • Client Clusterization Among Banking Clients
  • Voice Feature Analytics for Early Detection of Parkinson Disease in Speech
  • Player Retention in Mobile Gaming: Social Interactions
  • Optimizing Transport Simulation Systems
  • Smart Tourism
  • Volatility Clustering for Financial Risk Management
  • Augmenting Mobile Phone Data with Public Data for Credit Scoring
  • Dimensionality Models and Optimization of Network Distributions
  • Redundancy in Government Documents
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 recent professional placements:

  • Accenture - Consultant (Barcelona)
  • Agoda - Data Scientist (Bangkok, Thailand)
  • Criteo - Business Intelligence Analyst (Barcelona)
  • Kernel Analytics - Data Scientist (Barcelona)
  • King - Junior Data Scientist (Barcelona)
  • Morgan Stanley - Quantitative Model Developer (Budapest, Hungary)
  • Nine Connections - Machine Learning Developer (Amsterdam, Netherlands)
  • Rhode Island Innovative Policy Lab - Data Scientist (Providence, RI, USA)
  • Stratagem Technologies - Reinforcement Learning Research (London, UK)
  • UNICEF - Monitoring and Evaluation Specialist (Kinshasa, Congo)

Download first placement results for the Data Science Class of 2015 and Class of 2016

Data Science blog by Master's students
blog

 

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

Visit the Barcelona GSE Data Science Blog

Faculty

Students

Data Science Student Profile 2018-19

25 students from 17 countries (84% international)

Most represented countries this year:

  • Germany (5)
  • Spain (4)
  • United States (2)

Most common academic backgrounds:

  • Economics
  • Engineering
  • Mathematics
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Alumni

Data Science alumni career paths

An overview of Data Science career paths in the first three cohorts (Class of 2015, 2016, 2017)

  • Most common job titles:
    Data Analyst, Data Scientist, Consultant
  • Employers who hired graduates include:
    Accenture, Agoda, Kernel Analytics, King, Morgan Stanley, UNICEF, among others
  • Cities with most Data Science alumni:
    Barcelona, London, Budapest
Placement by industry
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    alumni

    Aimee Barciauskas '16

    Data Engineer
    Originally from
    Seattle, WA
    United States
    Currently living in
    Washington, DC
    United States
    I chose the Barcelona GSE Master's in Data Science because I was impressed by the faculty and the program of courses. It was important to me the program emphasized mathematical theory. It’s relatively easy to teach yourself data visualization, coding and to apply different machine learning algorithms - so I knew it was important to focus on the “hard stuff” while in school. For me this would be the mathematical theory and proofs behind why the models and methods work.
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    alumni

    Sílvia Ariza '15

    Product Manager - Big Data Intelligence
    Originally from
    Lleida
    Spain
    Currently living in
    Barcelona
    Spain
    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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    alumni

    Laura Cozma '15

    Analyst
    Originally from
    Iași
    Romania
    Currently living in
    Barcelona
    Spain
    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!