Generative AI for Data Science and Data Teams

Generative AI is shifting how data professionals handle analysis, pipeline construction, and model deployment. This text-based course equips data scientists and analytics teams with the foundational understanding and modern strategies required to adapt and thrive in an AI-driven workflow. You will explore the shift from traditional model development to data-centric AI, discovering how large language models, retrieval systems, and intelligent tooling integrate into modern data architectures. What you'll learn: - Understand core generative AI terminology, architecture concepts, and data-centric paradigms - Learn how retrieval-augmented generation (RAG) and vector databases enhance data retrieval - Apply structured prompt engineering techniques to automate data preparation and transformation - Explore modern workflows for synthetic data generation and data quality augmentation - Design collaborative data team practices that incorporate AI-assisted analysis and automation - Implement evaluation methods and governance standards for AI-driven data outputs The course begins with fundamental definitions and conceptual frameworks before guiding you through structured text explanations and real-world implementation patterns. It is designed for beginners, data analysts, and team members looking to modernize their technical foundation without prior experience in generative AI. Start reading today to bring generative AI techniques directly into your data practice.