Generative AI for Data Engineering and Analytics

The role of the data professional is changing rapidly with the rise of artificial intelligence. To stay competitive, data engineers and analysts must learn how to integrate generative AI models into their daily development and analytical workflows. In this course, you will transition from traditional data workflows to AI-augmented engineering practices. You will learn how to automate repetitive coding tasks, parse unstructured data efficiently, and build intelligent data pipelines using Python and API integrations. By the end of this guide, you will be able to confidently apply generative models to solve complex data challenges faster and with greater accuracy. What you'll learn: - Understand the fundamental principles of generative AI and prompt engineering tailored specifically for data tasks - Generate and optimize SQL queries, database schemas, and comprehensive data documentation - Extract structured information from unorganized, raw textual data sources using Python - Build automated data pipelines that leverage APIs to transform and enrich datasets - Explore basic retrieval-augmented generation (RAG) concepts to connect language models to your database systems - Apply best practices for validating AI-generated data to ensure pipeline reliability and quality This course begins with foundational concepts of generative models and prompt design before progressing to hands-on pipeline integration and unstructured data processing. You will read through written explanations and analyze practical code snippets designed to be applied immediately to real-world data environments. This course is designed for beginner to intermediate data engineers, analysts, and scientists who want to integrate AI into their workflows. A basic familiarity with Python and SQL is recommended, but no prior experience with artificial intelligence or machine learning is required. Start modernizing your data workflows with the power of generative AI today.