Databases and SQL for Data Science

Data science relies on clean, accessible, and well-structured data, yet querying and managing databases is often a major bottleneck for analysts. Understanding how to design, query, and optimize databases is essential for extracting actionable insights efficiently. In this written course, you will transition from writing basic queries to understanding database architecture and design principles specifically optimized for data science. You will gain the skills to structure tables, write complex analytical SQL, and work with modern data systems. What you'll learn: - Understand core relational database concepts, schemas, and data modeling principles. - Write efficient SQL queries to filter, aggregate, and join data from multiple tables. - Design relational database schemas that maintain data integrity and optimize query performance. - Explore NoSQL databases and understand when to use non-relational storage for unstructured data. - Analyze data using advanced SQL functions, including window functions and analytical operations. - Discover the fundamentals of vector databases and how they support modern machine learning and AI workflows. The course begins with foundational database terminology, normalization rules, and relational theory. You will then progress through practical SQL query construction, database design principles, and modern data storage paradigms through clear text-based explanations and query examples. This course is designed for aspiring data scientists, data analysts, and beginners who want to build a strong foundation in database management without any prior engineering experience. Start building and querying databases to power your data science projects today.