The Data Science Education Podcast

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“It was in the 1970s that David Friedman and his colleagues completely changed the way statistics is taught in the world, from going from just an emphasis on calculation, calculation, calculation, without really paying any attention to, what's the question, and what can you do with the answer?… Why does anyone care? What is the calculation that you can justifiably do, given the information at hand? And then how do you interpret the answer? That is traditional statistics teaching, and I haven't strayed one step away from it. I'm still there. It's called data science now. The tools are different. And because the tools are different, we are empowered to ask questions that we wouldn't have dared to ask before. And we can answer it in ways that we couldn't before. But I still think I am teaching traditional statistics.”

Today, we sit down with Ani Adhikari, a pioneer in building data science at UC Berkeley. She explains that traditional statistics education at Berkeley has always emphasized conceptual understanding, which she continues to aim to bring to the data science curriculum. Discussing teaching methods, she reassures statistics educators transitioning into data science that they don’t need to fundamentally change their approach—just the tools they use. Looking towards the future, Ani emphasizes AI’s rapid development, stressing the importance of equipping students with fundamental reasoning skills that will remain relevant regardless of how the industry continues to change. She ends by urging fellow educators to respect the history of data science, build on it, and remain aligned with their own intellectual and philosophical teaching goals.

“Think about why: why are you wanting to be a data science educator? That answer will be very different for many people. But trying to get to the core of that answer is the key. What is your intellectual, philosophical reason? And then make sure that everything you do, you always ask yourself: am I achieving those philosophical intellectual goals that I had? And please, please, please respect the history. Do not think of data science education as something brand new. It has been happening since people started making decisions… Know the history, respect the history, and build on it. And then you will be fulfilled, and so will your students be.”