The rise of artificial intelligence resurfaces a question older than the abacus: If we have a tool to do it for us, why learn to do it ourselves? 

The answer, argues MIT electrical engineering and computer science (EECS) Professor Devavrat Shah, hasn’t changed: Foundational skills in mathematics remain essential to using tools well, from knowing which tool to use to interpreting results correctly.

“As large language models and generative AI meet new applications, these cutting-edge tools will continue to reshape entire sectors of industry, and bring new insights to challenges in research and policy,” argues Shah. “The world needs people who can grasp the underlying concepts behind AI to truly leverage its potential.”

Shah is a professor in MIT’s Institute for Data, Systems, and Society (IDSS), a cross-disciplinary unit meeting the global need for data skills with online course offerings like the MicroMasters Program in Statistics and Data Science, which Shah directs. 

“With over a thousand credential holders worldwide, and tens of thousands more learners engaged since its inception, the MicroMasters Program in Statistics and Data Science has proven to be a rigorous but flexible way for skilled learners to develop an MIT-level grasp of statistics fundamentals,” says Shah.

The MicroMasters also forms the backbone of IDSS education partnerships, where an embedded MIT team collaborates with organizations to support groups of learners through the MicroMasters curriculum.

“Together with our first strategic partner in education, IDSS is providing graduate-level data science education through the Brescia Institute of Technology (BREIT) in Peru,” explains Fotini Christia, the Ford International Professor of the Social Sciences at MIT and director of IDSS. “Through this partnership, IDSS is training data scientists who are informing decision-making in Peruvian industry, society, and policy.”