Data Warehousing Fundamentals for Data Science and AI

To build reliable AI models and accurate data science pipelines, you first need a solid foundation of clean, structured, and easily accessible data. This written course teaches you how to design, build, and navigate modern data warehouses specifically tailored for data science and artificial intelligence applications. You will transition from understanding raw data storage to confidently querying and structuring complex datasets for advanced analytics. What you'll learn: Understand core data warehousing terminology, architectures, and the fundamental differences between OLTP and OLAP systems; Design clean data models using star and snowflake schemas optimized for analytical queries; Apply modern ETL and ELT pipeline patterns to clean, transform, and load data efficiently; Explore cloud data warehouse architectures like Snowflake and BigQuery and how they integrate with machine learning workflows; Learn how to structure data specifically to feed feature stores and train AI models; Practice querying data warehouses using SQL to extract clean datasets for analysis. This course begins with essential terminology and foundational definitions before guiding you step-by-step through database design, storage concepts, and modern cloud integration patterns. It is designed entirely for beginners with no prior data engineering experience required. Start reading today to build the data infrastructure skills that power modern AI.