Lesson 5.2 – SQL for Business Analytics | SELECT, JOIN, GROUP BY & HAVING

Learning Objectives

After completing this lesson, you will be able to:

• Understand the role of SQL in Data Analytics.
• Explain the basics of relational databases.
• Differentiate between databases, tables, rows, and columns.
• Write basic SQL queries using the SELECT statement.
• Filter data using the WHERE clause.
• Sort query results using ORDER BY.
• Limit query results using the LIMIT clause.
• Use column aliases to improve query readability.

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Introduction

Organizations collect enormous amounts of data every day, including customer information, product sales, financial transactions, website activity, employee records, and inventory details. This data is usually stored in relational databases rather than spreadsheets because databases provide better security, scalability, and performance.

Structured Query Language (SQL) is the standard language used to communicate with relational databases. Data Analysts use SQL to retrieve, filter, summarize, transform, and analyze data before creating reports and dashboards.

SQL is considered one of the most important technical skills for Data Analysts because nearly every organization stores business data in databases such as MySQL, PostgreSQL, Microsoft SQL Server, Oracle Database, or SQLite.

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What is SQL?

SQL (Structured Query Language) is a standard programming language used to manage and manipulate data stored in relational databases.

SQL allows users to:

• Retrieve data from databases.
• Filter records based on conditions.
• Sort data.
• Calculate summaries.
• Combine multiple tables.
• Insert, update, and delete records.
• Create reports for business decision-making.

Unlike programming languages such as Python or Java, SQL is specifically designed for working with structured data.

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Why is SQL Important for Data Analytics?

Most organizations store their operational and transactional data inside relational databases. Data Analysts use SQL to access this information and convert raw data into meaningful business insights.

SQL is important because it enables analysts to:

• Retrieve only the required data.
• Work efficiently with millions of records.
• Generate reports quickly.
• Combine data from multiple tables.
• Perform calculations directly inside the database.
• Reduce manual data processing.
• Prepare data for visualization tools such as Power BI and Tableau.

Learning SQL is an essential step toward becoming a successful Data Analyst.

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What is a Relational Database?

A relational database stores data in tables that are related to one another through common fields called keys.

Each table contains rows and columns, making the data easy to organize, retrieve, and analyze.

Example:

An online shopping company may have separate tables for:

• Customers
• Orders
• Products
• Payments
• Suppliers

These tables are connected using unique identifiers such as Customer ID or Product ID.

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Understanding Tables, Rows, and Columns

Table

A table is a collection of related data organized into rows and columns.

Example: Customers Table

Customer_ID	Name	City	Age
101	Amit	Delhi	28
102	Priya	Mumbai	31

Row (Record)

Each row represents one complete record.

Column (Field)

Each column represents one attribute of the data.

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Popular SQL Database Management Systems

Several database management systems support SQL.

Database	Common Use
MySQL	Web Applications
PostgreSQL	Enterprise Analytics
Microsoft SQL Server	Business Intelligence
Oracle Database	Large Enterprise Systems
SQLite	Mobile and Desktop Applications

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Basic SQL Query Structure

A basic SQL query generally follows this structure:

SELECT column_name
FROM table_name
WHERE condition
ORDER BY column_name;

Each clause performs a specific task in retrieving and organizing data.

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The SELECT Statement

The SELECT statement retrieves data from one or more tables.

It is the most frequently used SQL command in Data Analytics.

Select All Columns

SELECT *
FROM Customers;

This query returns every column and every row from the Customers table.

Select Specific Columns

SELECT Customer_ID,
       Name,
       City
FROM Customers;

This query returns only the selected columns.

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Using Column Aliases (AS)

Aliases make query results easier to understand by assigning meaningful names to columns.

SELECT Customer_ID AS CustomerNumber,
       Name AS CustomerName
FROM Customers;

The original database structure remains unchanged while the output becomes more readable.

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The WHERE Clause

The WHERE clause filters records based on specified conditions.

Instead of retrieving every record, analysts can focus only on relevant data.

Example 1

SELECT *
FROM Customers
WHERE City = 'Delhi';

This query returns only customers located in Delhi.

Example 2

SELECT *
FROM Employees
WHERE Salary > 60000;

This query retrieves employees earning more than 60,000.

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Comparison Operators

Operator	Description
=	Equal To
>	Greater Than
<	Less Than
>=	Greater Than or Equal To
<=	Less Than or Equal To
<>	Not Equal To

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Logical Operators

SQL also supports logical operators for combining conditions.

Operator	Purpose
AND	Both conditions must be true.
OR	At least one condition must be true.
NOT	Reverses a condition.

Example

SELECT *
FROM Customers
WHERE City='Delhi'
AND Age > 25;

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ORDER BY Clause

The ORDER BY clause sorts query results in ascending or descending order.

Ascending Order

SELECT *
FROM Products
ORDER BY Price ASC;

Descending Order

SELECT *
FROM Products
ORDER BY Price DESC;

Sorting makes reports easier to interpret.

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LIMIT Clause

The LIMIT clause restricts the number of rows returned by a query.

Example

SELECT *
FROM Products
LIMIT 10;

This query returns only the first 10 records.

Analysts frequently use LIMIT when exploring large datasets.

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Combining SELECT, WHERE, ORDER BY, and LIMIT

The following query combines multiple SQL clauses.

SELECT Product_Name,
       Sales_Amount
FROM Sales
WHERE Sales_Amount > 50000
ORDER BY Sales_Amount DESC
LIMIT 5;

This query returns the top five products with sales greater than 50,000.

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Real-World Example

An e-commerce company stores millions of customer transactions in a MySQL database.

A Data Analyst needs to identify the highest-value customers in Delhi.

The analyst writes the following SQL query:

SELECT Customer_Name,
       Total_Purchase
FROM Customers
WHERE City='Delhi'
ORDER BY Total_Purchase DESC
LIMIT 10;

The query instantly retrieves the top ten customers based on total purchases, helping the marketing team identify premium customers for loyalty programs.

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Common Mistakes Beginners Make

• Using SELECT * unnecessarily.
• Forgetting the WHERE clause and retrieving too much data.
• Ignoring aliases, making reports difficult to read.
• Sorting data in the wrong order.
• Using incorrect quotation marks for text values.

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Continue to Part 2, where you will learn Aggregate Functions, DISTINCT, GROUP BY, HAVING, SQL Operators, and SQL JOINs with practical business examples.