The demand for Data Scientists is exploding, driven by the increasing availability of data and the advance of machine learning. Data collection and analysis have become crucial components of decision making in today’s private and public organizations and many have started to develop specialized departments for this purpose.

The ability to extract, handle, and analyze large amounts of data is therefore a key skill on today’s job market.

However, gathering and summarizing data is not enough. Data science can only improve decision making with an understanding of how choices affect outcomes. Data Scientists must therefore increasingly combine standard tools in machine learning with an understanding of the causal relationships behind the data.

The BSE Master's Program in Data Science for Decision Making integrates key elements from Data Science and Economics to give graduates the ability to deal with all types of data and make the correct inferences from it.

Students will be trained in the use of cutting-edge machine learning methods and of statistical models that will help them to provide effective data support in the decision-making process of any organization. Students will, for example, be able to extract information from the structure of social networks, satellite images, large libraries of digitized text, read and visualize data in maps and make sense of geo-localized information and time series data. They will also be able to use this data for forecasting and evaluate different policy options or business strategies through models built on an understanding of causal relationships.

Study with leading researchers and practitioners

Data Science for Decision Makers integrates different approaches from Data Science, Statistics, Econometrics and Economics in a unique program taught by leading academics in the fields of Economics, Operations, and Statistics, as well as experienced professionals from the analytics industry and public policy consultants from organizations.​

Work with real data on interdisciplinary teams

Students learn with real data to solve decision-making problems hands-on through homework, applied training sessions, and an independent Master's project.

The collaborative environment of the program will expose students to working with colleagues from many different academic and professional backgrounds on interdisciplinary teams.

The Data Science for Decision Making Master's offers a special way to bring skills we teach in the program to real-life problems: the Barcelona School of Economics Master's Thesis Challenge! The challenge allows outside organizations to submit problems that are then tackled by small student groups. The solution is then submitted as a Master's thesis and presented in front of the respective organization.

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

Is “Data Science for Decision Making” for you?

The Decision Making track is tailored to accommodate both recent graduates and those with work experience in private and public organizations. All students will benefit from a combination of training in programming, theoretical model techniques and hands-on applications which will allow them advance any decision making process in any organization. Students with work experience will be able to see the data in their own work environment with completely new eyes and realize the potential of untapped data resources like text and images.