"Generative AI in Data Science: Models, Methods, and Use Cases* provides a comprehensive exploration of how generative artificial intelligence is transforming the landscape of data science. The book introduces readers to the foundational principles of generative models, including probabilistic approaches, deep learning architectures, and modern techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models. Structured to balance theory and application, the book explains how these models learn patterns from data and generate realistic outputs such as images, text, and synthetic datasets. It further discusses key methodologies, model training strategies, evaluation techniques, and challenges such as bias, overfitting, and data privacy. A significant focus is placed on real-world use cases, demonstrating how generative AI is applied across domains like healthcare, finance, marketing, and computer vision. From data augmentation and anomaly detection to content generation and decision support systems, the book highlights practical implementations that bridge the gap between research and industry. In addition, ethical considerations and responsible AI practices are addressed, encouraging readers to develop solutions that are both innovative and trustworthy. Designed for students, researchers, and professionals, this book serves as a valuable resource for understanding and applying generative AI techniques within data science."