Topological data analysis (TDA) is a method that uses techniques from topology to study the shape of data, providing insights into the structure and features of high-dimensional datasets. It is particularly useful for identifying clusters, holes, and voids in data that traditional statistical methods might overlook, offering a robust way to analyze complex data sets in various fields like biology, neuroscience, and machine learning.