Generative AI for Data Science: Augmentation and Engineering

As generative artificial intelligence reshapes the technology landscape, data professionals must adapt to stay competitive. Understanding how to leverage these powerful models can transform how you handle data scarcity, feature creation, and predictive modeling. This written course equips you with the practical knowledge to integrate generative AI directly into your data science workflows. You will transition from traditional data manipulation to advanced techniques that use large language models and generative patterns to solve complex, real-world data challenges. What you'll learn: - Understand the foundational concepts of generative AI and how these models process structured and unstructured data. - Apply prompt engineering techniques specifically optimized for data extraction, cleaning, and classification tasks. - Generate high-quality synthetic datasets to address data scarcity and improve model training balance. - Engineer advanced features using modern large language model APIs and vector embeddings. - Evaluate the quality, bias, and ethical considerations of AI-generated synthetic data. The course begins with core terminology and generative AI fundamentals before moving into practical, text-based guides and code snippets. You will explore step-by-step written explanations of data augmentation and feature engineering workflows that you can apply immediately to your own projects. This course is designed for aspiring and practicing data scientists, analysts, and engineers who want to add generative AI to their toolkit. It starts with the absolute basics, making it accessible to anyone with a foundational understanding of data concepts. Start reading today to modernize your data science skill set and unlock new analytical capabilities.