Course description

This self-paced certificate course teaches practical SQL skills you can use immediately to analyze structured data, build reports, and support data-driven decisions. Through concise lessons, real-world examples, and interactive exercises you'll learn how to retrieve and filter data, transform and aggregate results, combine multiple tables, and apply advanced techniques such as window functions and common table expressions (CTEs).

The course emphasizes hands-on practice: each module includes downloadable sample datasets and guided lab problems (compatible with PostgreSQL, MySQL, and SQLite). You'll also learn best practices for writing readable, efficient queries and basic query optimization techniques useful when working with large datasets. A final applied project and a multiple-choice assessment validate your skills and, upon passing, you earn a professional certificate to showcase on LinkedIn or your resume.

Ideal for analysts, product managers, researchers, healthcare professionals, and anyone who works with tabular data — no prior SQL experience required. By the end of the course you'll be able to extract insights from relational databases, prepare data for visualization or modeling, and automate common analysis tasks using SQL.

What will i learn?

• Construct correct and efficient SELECT queries to retrieve and filter data using WHERE, ORDER BY, and LIMIT.
• Use aggregate functions (COUNT, SUM, AVG, MIN, MAX) with GROUP BY and filter groups with HAVING.
• Join multiple tables using INNER, LEFT, RIGHT/FULL joins and choose the appropriate join type for analysis tasks.
• Apply subqueries and Common Table Expressions (CTEs) to organize complex queries and improve readability.
• Use window functions (ROW_NUMBER, RANK, LEAD, LAG, SUM OVER) to compute rankings, running totals, and moving averages.
• Create calculated fields and use date/time functions for time-series analysis.
• Perform basic data transformation and ETL patterns within SQL (e.g., string manipulation, type casting, deduplication).
• Interpret query execution basics and apply simple optimizations (indexes, limiting scanned rows, avoiding unnecessary computations).
• Design and complete an applied analysis project: extract, clean, analyze, and summarize insights from a relational dataset and present results.
• Produce reproducible, well-documented SQL scripts suitable for integration with reporting tools or pipelines.

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