Structured Query Language (SQL) is the backbone of data handling across industries, making it a vital skill for developers, analysts, data scientists, and IT professionals. This course, "Mastering SQL – From Fundamentals to Advanced Querying," is designed to take learners from the foundational concepts of relational databases to advanced SQL techniques used in enterprise environments.
Whether you're a beginner with no prior experience or a developer looking to deepen your database querying skills, this course offers a comprehensive, hands-on approach to understanding and applying SQL in real-world scenarios.
By the end of the course, you’ll not only be able to write complex queries but also interpret, optimize, and debug them effectively—skills that are indispensable in data-driven roles.
Target Audience
This course is designed for:
Aspiring data analysts and data scientists
Backend and full-stack developers
IT professionals and database administrators
Business analysts and product managers
Students and graduates in computer science or data-related fields
No prior knowledge of SQL or databases is required, though basic programming logic will be helpful.
Course Objectives
Upon successful completion of this course, learners will be able to:
Understand the structure and functionality of relational databases
Design, query, and manage databases using SQL
Write and optimize SQL queries for CRUD operations (Create, Read, Update, Delete)
Perform advanced queries using JOINs, subqueries, and set operations
Apply grouping, filtering, and aggregation functions
Use window functions and common table expressions (CTEs)
Work with date and string functions to manipulate and analyze data
Ensure data integrity using constraints and transactions
Identify and resolve performance bottlenecks in SQL queries
Course Modules
Module 1: Introduction to Relational Databases
What is a database?
Understanding relational models
Tables, rows, columns, and keys (Primary, Foreign)
Introduction to SQL as a query language
Module 2: Getting Started with SQL
Setting up a database environment (MySQL/PostgreSQL/SQLite)
Using an SQL client or interface
SELECT statements and filtering data with WHERE
Sorting results with ORDER BY
Limiting results with LIMIT and OFFSET
Module 3: Data Definition Language (DDL)
Creating and modifying tables (CREATE, ALTER, DROP)
Data types and column attributes
Constraints (NOT NULL, UNIQUE, DEFAULT, CHECK)
Primary and foreign key relationships
Module 4: Data Manipulation Language (DML)
Inserting data into tables
Updating records
Deleting records safely
Best practices for data integrity
Module 5: Working with Functions and Operators
Arithmetic and logical operators
String functions (CONCAT, LENGTH, SUBSTRING, etc.)
Numeric functions (ROUND, CEIL, FLOOR)
Date/time functions (NOW, DATE_ADD, DATEDIFF, etc.)
Module 6: Aggregation and Grouping
COUNT, SUM, AVG, MIN, MAX
GROUP BY and HAVING clauses
Filtering aggregated results
Nested aggregation and derived columns
Module 7: JOINS – Combining Data from Multiple Tables
INNER JOIN vs LEFT/RIGHT/FULL OUTER JOIN
CROSS JOIN and SELF JOIN
Best practices for writing efficient joins
Real-world join scenarios (orders, users, products)
Module 8: Subqueries and Set Operations
Scalar, correlated, and inline subqueries
Using subqueries in WHERE, FROM, and SELECT clauses
Set operations: UNION, INTERSECT, EXCEPT
Performance considerations for subqueries
Module 9: Advanced SQL Techniques
Common Table Expressions (CTEs) and recursive queries
Window functions: RANK, DENSE_RANK, ROW_NUMBER, LEAD/LAG
Analytical queries using PARTITION BY and ORDER BY
Working with views and materialized views
Module 10: Transactions and Data Integrity
Understanding transactions and ACID properties
BEGIN, COMMIT, ROLLBACK
Using constraints and triggers
Handling concurrency and locking
Module 11: Query Optimization and Best Practices
Indexing and its impact on performance
EXPLAIN plans and interpreting them
Writing readable, maintainable SQL
Common pitfalls and how to avoid them
Module 12: Capstone Project
Design a normalized database schema
Populate the database with realistic sample data
Write a series of queries to generate business insights
Present your project as a report or dashboard-ready dataset
