The recent hype of artificial intelligence has led many AI companies to struggle or fail, frequently due to their poor data management. This course will serve as an introductory step towards modern practices for data management, starting from data persistence, data encoding and organization, database management tasks, data models (e.g., Ted Codd's relational model), modern query languages (e.g., SQL), anomalies from data redundancy, logical database design, data caching, query acceleration (e.g., data skipping, indexes), transaction management, concurrency control protocols (e.g., 2PL, OCC, MVCC), logging schemes (e.g., WAL), recovery mechanisms (ARIES), and database administrative tasks (e.g., database migration, version upgrading, backup, access control). Most of these topics will be associated with practical labs leveraging modern data management tools (e.g., DuckDB, Polars).