SQL (Structured Query Language) is a core tool used by data analysts to work with structured data stored in relational databases. It enables efficient data retrieval, filtering, aggregation and transformation to generate meaningful insights for business decision-making.
- Writing SQL queries to extract and analyze data.
- Using aggregation, joins and analytical functions.
- Applying SQL for reporting and business insights.
1. Introduction to Data Analysis
Data analysis is the process of examining, cleaning, transforming and summarizing data to discover patterns, trends and insights. SQL plays an important role in this process by enabling analysts to interact directly with structured data stored in relational databases.
2. Getting Started with SQL
This section covers the basics of SQL, including setting up databases (like MySQL or PostgreSQL), understanding relational databases and executing essential SQL commands like SELECT, INSERT, UPDATE and DELETE. The goal is to learn how to interact with databases and retrieve the data needed for analysis.
- Introduction
- Installing MySQL/PostgreSQL
- Understanding SQL Commands
- SQL CREATE DATABASE
- SELECT
- SQL INSERT INTO
- SQL UPDATE
- SQL DELETE
- SQL ALTER TABLE
- DROP and TRUNCATE
3. Basic SQL Queries for Data Analysis
Here, you’ll learn how to use SQL to retrieve specific data from databases. Key topics include selecting columns, filtering records with WHERE clauses, using logical operators and sorting data with ORDER BY. Basic SQL queries are the foundation for data extraction and analysis
- Select Distinct
- Select Individual Columns
- WHERE Clause
- HAVING Clause
- BETWEEN & IN Operator
- Comparison Operators
- Logical Operators
- NULL Operator
- NULL values
- ORDER BY
- Multiple Column Ordering
- LIMIT Clause
- TOP, LIMIT, FETCH FIRST Clause
- Aliases
4. Aggregate Functions and Grouping Data
SQL aggregate functions (e.g., COUNT(), SUM(), AVG(), MAX(), MIN()) are essential for summarizing data. Grouping data with the GROUP BY clause allows you to aggregate data into meaningful subsets (e.g., total sales by region). This section teaches you how to aggregate and analyze grouped data.
5. Joining Data from Multiple Tables
Often, data is spread across multiple tables. SQL joins, such as INNER JOIN, LEFT JOIN and RIGHT JOIN, allow you to combine data from different tables based on related columns. This section explains how to use joins to link data and perform cross-table analysis.
6. Advanced Data Analysis Techniques in SQL
Let's delves into more complex SQL techniques, such as window functions, subqueries and common table expressions (CTEs). These methods allow for more sophisticated analysis, like running totals or ranking data, to uncover deeper insights from large datasets.
- Subquery
- Nested Queries
- Subquery in a SELECT Statement
- Sub queries in From Clause
- Correlated Subqueries
- Subqueries in the WHERE Clause of UPDATE
- CTE
- Views
7. Data Cleaning and Transformation in SQL
Data cleaning is an essential step in analysis and SQL provides functions to handle missing values (e.g., IS NULL, COALESCE), remove duplicates (DISTINCT) and transform data (e.g., CONCAT(), date manipulation). This section covers how to clean and preprocess data to ensure accuracy and consistency before analysis.
- Query to Delete Duplicate Rows
- Remove Duplicates without Distinct
- NULL functions
- IFNULL VS COALESCE
- Conversion Function
- Data Types
- Modifying existing data
- Date and Time Functions
- Get Current Date and Time
- Query to Check Given Format of a Date
- String functions
- Character Functions with Examples
- Concatenation Operator
- Query to Match Any Part of String
8. Advanced SQL Queries for Data Analysts
Now, let's cover more advanced SQL queries, including nested queries, complex joins and query optimization techniques. These queries are useful for handling large datasets and extracting meaningful insights, such as calculating complex metrics or filtering data with specific conditions
- Performance Tuning
- SQL Query Optimizations
- Indexes
- Query Execution Plan
- Query-Evaluation Plan
- Query Processing
- Hierarchical Data and How to Query
- Transforming Rows to Columns
- Pivot and Unpivot
9. SQL for Reporting and Data Visualization
SQL is not only used for analysis but also for reporting. This section explains how to use SQL to generate reports, prepare data for visualization and integrate SQL with data visualization tools like Tableau or Power BI. It emphasizes using SQL to prepare datasets for actionable insights and visual representation.
- Query to Make Month Wise Report
- SELECT DATE
- Visualization Tools for Data Engineers
- Data Analytics Training using Excel, SQL, Python & PowerBI
10. Performance Tuning and Best Practices
As datasets grow, query performance becomes more important. This section covers techniques like indexing, query optimization and using efficient SQL functions to enhance performance. Best practices in writing SQL queries for optimal performance will help you work more efficiently with large datasets.
- Writing Efficient SQL Queries
- How to Limit Query Results
- SQL Queries on Clustered and Non-Clustered Indexes
- EXPLAIN
11. SQL for Advanced Data Analysis Tasks
Explore SQL's role in handling advanced data analysis tasks such as predictive modeling, time-series analysis and complex data manipulations. It focuses on how to use SQL for sophisticated analysis beyond basic querying and aggregation.
12. SQL Exercises, Projects and Interview Questions
Finally, hands-on exercises, projects and commonly asked interview questions to help you practice and apply your SQL skills. Working on real-world projects and solving problems will help reinforce your learning and prepare you for SQL-based job roles.