In this course, you will learn how to improve computer vision performance by optimizing the dataset before model training begins. You will examine how dataset characteristics such as class distribution, image resolution, aspect ratio, channel statistics, blur, corruption, and deployment gaps shape the choices you make about model families and preprocessing pipelines. You will move from analysis to action by selecting practical strategies for resizing, normalization, deduplication, and transfer learning based on the data you actually have. You will also learn how to use image augmentation to increase dataset diversity, reduce overfitting, and improve generalization without collecting new labeled data. Through examples and applied activities, you will evaluate semantic validity, match augmentation techniques to real dataset gaps, and design training-only pipelines that reflect deployment conditions. By the end of the course, you will have a structured, repeatable approach to analyzing and augmenting vision datasets so you can build more robust and reliable computer vision systems.