Data Science for Decision Making Program

The demand for Data Scientists is growing rapidly, driven by the increasing availability of data and advancements in machine learning. At the same time, decision-making in business, policy, and research requires more than just algorithms; it requires understanding the mechanisms—economic, institutional, and social—that generate the data in the first place.

The Master’s Program in Data Science for Decision Making at the Barcelona School of Economics brings these two worlds together. Unlike general data science degrees, this program combines advanced computational methods with structured thinking about decisions and outcomes. The result is a unique skill set that allows graduates to not only handle complex data but also to translate it into meaningful insights for organizations, governments, and society.

Why combine Data Science and Economics?

Collecting and summarizing data is not enough. We strongly believe that students must not only learn to run algorithms, but to understand why, when, and where they are appropriate. Data are not just numbers—they emerge from strategic, institutional, and market interactions. Therefore, data science improves decision-making only when paired with an understanding of how choices, incentives, and institutions shape outcomes. By combining modern machine learning techniques with the interpretability of social science and economic models, graduates of the program develop a competitive advantage in areas such as:

• Optimal pricing and market design where they combine prediction with incentive-based modeling
• Demand and behavior forecasting by using microdata, time-series, and ML to anticipate outcomes
• Networks and platforms through the analysis of  ripple effects in social, digital, and organizational systems
• Policy evaluation by linking predictive models with counterfactual analysis to assess interventions
• Spatial and geo-information analysis through gaining insights from satellite images, geo-localized events, and mobility data
• Text and unstructured data by transforming documents, archives, and news into structured evidence

This combination offers  the best of both fields: powerful tools in data management, machine learning and generative AI, combined with the clear insights  of economic and social science.

Program Highlights

Advanced training

Students learn modern machine learning, econometrics, and computational statistics alongside decision-making and modeling approaches used in economics and the social sciences.

Practical learning

Students will work with real data – networks, satellite images, text archives, maps, and time-series. They will  practice extracting, visualizing, and analyzing data to inform real-world decisions.

Integration of social science and machine learning

The program combines the strengths of machine learning with those of economics and other social sciences. Students are not only able to tackle all data types but they also understand how to model the data generating process for decision making support.

Master’s Thesis Challenge

The BSE Data Science Challenge is a hallmark of the program. Student teams develop solutions to real problems presented by private and public organizations.. The outcomes are developed under the close supervision of Professors from the program to maximize learning and impact.

Here are some of the organizations BSE student teams are working with:

• Columbian Energy – Predictive Modeling for Day-Ahead Pricing in Electricity Markets: A Methodological Approach for Colombia
• Glovo – Analyzing the Impact of Stock-Outs on Sales of Products in an Online Delivery Application
• Innova – BiciMAD: Bike-Sharing System Dataset Creation and Preliminary Hourly Demand Prediction using Machine and Deep Learning Approaches
• Koa Health – Behavioural predictors of personality inferred by passive smartphone sensing
• Novartis – Decoding Abnormal Returns: Unraveling Insights from Pharmaceutical Sector Earnings Calls through Graph-Enhanced Text Analysis
• Novartis – Comparative Analysis of Modeling Approaches for Value-at-Risk Forecasting in the Pharmaceutical Sector: An econometric and ML approach
• Oxera – Automated Feature Extraction from EU Merger Documents: Novel Approaches using LLMs
• UNDP / UNICEF – Harnessing Big Data News Media For Conflict Prediction and Anticipatory Decision-Making
• UNHCR – Forecasting Global Refugee Flows: A Machine Learning approach using non-conventional data
• Virtuleap – Harnessing Big Data News Media For Conflict Prediction and Anticipatory Decision-Making

World-class faculty and practitioners

The program is taught by leading academics in Economics, Statistics, and Operations, alongside professionals from industry, consultancies, and public organizations.

Collaborative environment

By working in interdisciplinary teams, students gain experience that mirrors the data-driven environments of modern firms, international organizations, and research centers.

Is this Master’s for you?

This program is designed for students and professionals who want to go beyond spreadsheets, basic statistics, or ad-hoc coding. Many of our applicants already work with data but feel frustrated by the limits of their current skills—they want to move from “just handling data” to building predictive models, extracting insights, and driving decisions.

Graduates leave with a unique combination of skills in programming, modeling, and applied analysis—prepared to lead data-driven decision-making in any organization.