Episode 88 of The Data Science Podcast dives into dimensionality reduction — but not for preprocessing. Lucas and Luna explore how data scientists use t-SNE and UMAP to visualize high-dimensional data, from customer segmentation to single-cell genomics. They discuss the trade-offs between global and local structure preservation, the risk of over-interpreting clusters, and why a 2D plot is never the whole truth. With concrete examples from retail analytics and biology, this episode gives you a practical framework for when to use t-SNE versus UMAP and how to avoid common pitfalls. If you've ever stared at a scatter plot and wondered if the patterns are real, this one's for you.

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