Database Management Systems (DBMS) for Data Science and AI

To build successful data science and AI models, you must first know how to store, query, and manage the underlying data. This text-based course guides you through the fundamental principles of Database Management Systems (DBMS) tailored specifically for data-driven professions. You will transition from understanding basic database definitions to writing queries, optimizing indexes, and exploring how modern AI systems leverage vector databases. Through clear written explanations, structured code examples, and practical theoretical exercises, you will develop a strong command of data storage architectures. What you'll learn: - Understand foundational database concepts, relational models, and schema design. - Write and analyze SQL queries to extract and manipulate datasets effectively. - Apply normalization techniques to ensure data integrity and reduce redundancy. - Explore indexing, transaction management, and query optimization for faster data retrieval. - Learn the basics of NoSQL and modern vector databases used in AI and machine learning workflows. - Practice database design principles through written scenarios and structural exercises. The course begins with core database terminology and relational theory before moving into practical SQL syntax, transaction control, and modern database paradigms. You will progress systematically from theoretical concepts to conceptual database design. Designed for aspiring data scientists, AI enthusiasts, and beginners looking to build a strong foundation in database systems, this course requires no prior database experience. Start reading today to master the database essentials that power modern data science and AI.