Data augmentation is the practice of artificially expanding a training dataset by creating modified copies of existing data: rotating or flipping images, paraphrasing text, adding noise to audio, or synthesizing new examples. It helps models generalize better and reduces overfitting, especially when collecting real data is expensive or impractical. A Moroccan hospital with few rare-disease X-rays can augment its dataset by applying rotations and brightness changes to existing scans.