As data science and AI evolve rapidly, the core habits of mathematical thinking remain unchanged. This talk explores how skills such as precise problem definition, questioning assumptions, reasoning with uncertainty, and testing conclusions are central to real-world data science.
Using examples from healthcare analytics, the session will show why good analysis is not just about coding or models, but about asking the right questions of the data.
The key message: tools will change, but mathematical thinking will remain fundamental to making reliable decisions in an AI-driven world.