Behind every powerful deep learning model lies a carefully crafted dataset—and in this episode, we unpack the science of getting it right. From splitting data into training, validation, and test sets to choosing between random, stratified, or time-based strategies, we reveal how these choices shape model performance. We’ll explore essential preprocessing steps like handling missing values, normalization, and one-hot encoding, before diving into data augmentation tricks across images, text, and time-series. Tune in to discover how thoughtful dataset prep fuels smarter, more resilient AI.