The digital revolution brings an explosion of data with significant value for businesses, science, and society. As data becomes larger and more complex, extracting useful quantitative insights becomes more challenging.
The Master's prepares graduates to design and build data-driven systems in the private, public and research sectors. The curriculum guides students from modelling and theory to computational practice and cutting edge tools, teaching skills that are in growing global demand.
The BSE Master's Program in Data Science Methodology aims to train future leaders in the field with a deeper understanding of the underlying methods, along with the ability to develop techniques for new and/or non-standard problems.
A distinguishing factor of the program is the faculty team, with a strong international research and applied reputation spanning multiple fields: Statistics, Operations Research, and Artificial Intelligence. Our professors routinely publish in top research journals in these disciplines and received prestigious awards and research funding from agencies and the industry. These include the main funding agencies in Spain, the European Research Council, the USA, and the UK, leading technological companies such as Google and Accenture, and banks such as BBVA and CaixaBank. The program also invites guest speakers and entrepreneurs working at the frontiers of Data Science Methods and applications.
- An undergraduate/bachelor/grado/laurea, or equivalent degree from an accredited college or university (for Bologna degrees, a minimum of 180 ECTS are required). A typical applicant holds a university diploma in Economics, Finance, Engineering, Mathematics, Statistics, or Business Administration, but students with academic backgrounds in other subjects have also been admitted.
- An advanced level of English language skills: TOEFL score of 90 or better / IELTS Academic Test score of 6.5 or better
- PhD Track Program only: GRE General Test score (for all other programs, GRE is optional but highlight recommended
Data Science Methodology students will gain a solid knowledge of Statistics, Machine Learning theory and methods such as Reinforcement Learning and Deep Learning, Optimization and Computing. The program also prepares students to critically assess the validity and performance of existing methods, and to address potential limitations by developing new ones. Students will learn how to apply classroom examples using real data and answering concrete questions from the perspectives of different sectors. Through an independent master's project or an industrial practicum conducted with local businesses or research teams, students have the opportunity to solve actual analytics problems hands-on.