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 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.
The program also invites guest speakers and entrepreneurs working at the frontiers of the Data Science.
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.
|All courses are mandatory.|
|Statistical Modelling and Inference||6||Omiros Papaspiliopoulos|
|Deterministic Models and Optimization||6||Marc Noy
|Data Warehousing and Business Intelligence||3||Guglielmo Bartolozzi|
|Computing Lab||3||Christian Brownlees|
|Economic Methods for Data Science||3||Christian Fons-Rosen
|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
|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
In 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.
|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.
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
In 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.
** Requires some prerequisite knowledge and authorization of the program director. Availability depending on sufficient demand.
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
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:
Examples of placements from the first class of Data Science graduates (Class of 2015):
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:
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.
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:
The main aim of the Data Science Summer School is to introduce participants to some of the tools and methods of Data Science.
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.