Singapore Management University (SMU) has launched the Master of Data Science in Economics (MDSE), Singapore's first and only master's programme that integrates data science and economics, to meet rising global demand for professionals who can apply artificial intelligence (AI) with domain expertise and analytical rigour.

As AI and machine learning (ML) become embedded in business and policy environments, the role of economists is evolving. Beyond building predictive models, there is increasing emphasis on interpreting outputs, assessing uncertainty, and understanding cause-and-effect relationships in complex, real-world data.

The MDSE is designed to address this shift. Through a curriculum that combines econometrics, AI and data science, students develop the ability to work with large-scale, multimodal datasets spanning numerical, textual and visual data, and to translate insights into decisions that carry economic and organisational impact.

"Globally, demand continues to grow for professionals with advanced skills in AI, machine learning and data science. At the same time, companies increasingly recognise the value of domain knowledge in economics," said Daniel Preve, Associate Professor of Economics (Education) and Programme Director, MDSE.

"While many data science programmes emphasise predictive modelling and deployment, the MDSE places additional focus on causal inference and predictive uncertainty. These capabilities are critical when decisions depend on understanding not just what is likely to happen, but why."

From predictive models to decision-ready insight 

A distinguishing feature of the MDSE is its emphasis on applying data science, AI, ML and econometric methods to support informed, accountable decision-making. Students are equipped to move beyond technical execution by learning to:

• Apply econometric, AI and ML models to real-world economic and financial datasets
• Distinguish between predictive and explanatory approaches, and understand when each is appropriate
• Evaluate model limitations and uncertainty in real-world decision contexts
• Communicate insights clearly to business and policy stakeholders