Teaching data analytics in the age of AI demands a fundamental rethink of what students need to learn, how they are assessed, and what analytical competence actually means when AI can write the code, run the models, and produce the results.

This episode explores the rapid collapse of traditional assessment, shares why taste may be the most valuable skill a data analyst can develop, and explains why working in teams of humans and AI agents is the direction the entire profession is heading. Our guest also reflects on how his Data Analysis with AI course at CEU has had to be redesigned with each passing semester and what universities must do differently if they are to stay relevant.

Our guest is Gabor Bekes, Professor of Economics and Programme Head of the MSBA at Central European University, Budapest. An applied economist whose research spans international trade, open source software collaboration, and industrial policy, Gabor is co-author of Data Analysis for Business, Economics, and Policy, published by Cambridge University Press, and has been teaching data analysis for over 15 years.

THINGS WE SPOKE ABOUT

- Why asking good questions matters more than choosing the right method

- The Data Analysis with AI course and what students discover

- How rapidly improving AI models overhauled the curriculum

- Taste as the emerging signal of analytical competence

- Zero-tech exams, dark factories, and the future of curriculum design

GUEST DETAILS

Gabor Bekes is Professor of Economics and Programme Head of the MSBA at Central European University, Budapest. An applied economist whose research spans international trade, open source software collaboration, and industrial policy, he is co-author of Data Analysis for Business, Economics, and Policy (Cambridge University Press) and has taught data analysis for over 15 years.