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“I think one of the things we've approached in our data science curriculum is this idea that data science is a team sport…You're never really doing data science on your own. You're always in a team and you're working with product managers. You're working with end users. You're working with software engineers. You're working with salespeople. And that idea of how do I translate people's problems? What is my system going to do? What are the variables and what considerations I have when I'm designing a system with people? What are the algorithms going to do and what does that mean? So that kind of idea of treating it as a team sport and figuring that out as a student, is like a fundamental principle for how we do data science in these environments.”

In this episode, we sit down with Paul Groth, Professor of Algorithmic Data Science at the University of Amsterdam. Throughout the episode, Paul shares the structure of data science and AI education in the Netherlands, highlighting how the Netherlands had AI undergraduate programs before data science became mainstream. He touches on the differences between AI education and data science, as well as his thoughts on treating scholarly publishing as structured data in his role as co-scientific director of the Discovery Lab. Finally, he ends with his approach to teaching and mentorship, approaching data science as a “team sport”.

“In the Netherlands, we taught AI before we ever taught data science. So actually, we have I think one of the first few places in the world where we have a bachelor's in AI. So I think it’s different from the US system, where you might start off with a general curriculum and then specialize in something like computer science. Here, our undergraduate students come straight from high school and go directly into a subject. So we have, for example, they can go directly into AI and then they'll do a three-year bachelor's in AI.”

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