🗄️ SQL Practice for Data Analytics

SQL exercises and analytical queries focused on Data Analytics, Business Intelligence, reporting, and business decision-making.

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🎯 About This Repository

This repository documents my SQL learning and practice through queries designed to solve analytical and business-oriented problems.

The objective is to develop SQL skills not only for retrieving data, but also for transforming raw information into:

• business metrics
• KPIs
• analytical reports
• performance indicators
• customer insights
• operational insights
• decision-support information

The exercises progress from fundamental SQL queries to more structured analytical scenarios.

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🔎 Topics

Basic Queries

• SELECT
• DISTINCT
• WHERE
• ORDER BY
• LIMIT
• aliases

Filtering and Conditions

• comparison operators
• AND / OR
• IN
• BETWEEN
• LIKE
• NULL handling

Aggregations

• COUNT
• SUM
• AVG
• MIN
• MAX
• GROUP BY
• HAVING

Data Relationships

• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• multiple-table queries
• relational data analysis

Conditional Analysis

• CASE WHEN
• conditional metrics
• customer and product classification
• business rules in queries

Intermediate SQL

• subqueries
• Common Table Expressions (CTEs)
• date functions
• string functions
• data transformation
• calculated fields

Analytical SQL

• window functions
• ranking
• running totals
• period comparisons
• customer segmentation
• performance analysis

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📊 SQL for Business Analytics

The focus of this repository is to connect SQL queries to real business questions.

Examples include:

BUSINESS QUESTION
       ↓
RELEVANT DATA
       ↓
SQL QUERY
       ↓
METRIC OR KPI
       ↓
ANALYSIS
       ↓
BUSINESS INSIGHT

Instead of asking only:

How do I write this query?

The analytical approach also asks:

What business question does this query help answer?

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💼 Business Questions

Queries in this repository may be used to explore questions such as:

• What is the total revenue by month?
• Which products generate the most sales?
• What is the average transaction value?
• Which customers purchase most frequently?
• Which customer segments generate the most revenue?
• Which products are underperforming?
• How is performance changing over time?
• What percentage of customers make repeat purchases?
• Which categories have the highest average sales?
• Where are unusual changes or performance deviations occurring?

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📈 KPI Analysis

SQL can be used to calculate and monitor business indicators such as:

• Total Revenue
• Sales Volume
• Average Order Value
• Conversion Rate
• Customer Retention
• Repeat Purchase Rate
• Revenue by Product
• Revenue by Customer
• Revenue by Period
• Growth Rate
• Operational Performance

The goal is to understand both how a KPI is calculated and what it represents in a business context.

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🧠 Example Analytical Logic

A typical analysis may follow this structure:

SELECT
    category,
    COUNT(*) AS total_orders,
    SUM(revenue) AS total_revenue,
    AVG(revenue) AS average_order_value
FROM sales
GROUP BY category
ORDER BY total_revenue DESC;

This type of query can help answer:

• Which categories generate the most revenue?
• Which categories have higher average transaction values?
• Is sales volume aligned with revenue performance?

The query itself is only part of the analysis. The next step is interpreting the result.

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🔄 From Query to Insight

DATABASE
    ↓
SQL QUERY
    ↓
FILTERING & TRANSFORMATION
    ↓
AGGREGATION
    ↓
METRICS
    ↓
ANALYSIS
    ↓
INSIGHT
    ↓
DECISION

This repository emphasizes SQL as an analytical tool rather than only a database language.

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🛠️ Skills Developed

Through these exercises, I am developing skills in:

• relational databases
• SQL syntax
• data filtering
• data aggregation
• joins
• analytical queries
• KPI calculation
• business metrics
• data validation
• reporting
• analytical thinking
• business problem solving

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🚀 Learning Roadmap

• SELECT statements
• Data filtering
• Aggregations
• GROUP BY
• Basic JOINs
• CASE WHEN
• Advanced JOINs
• Subqueries
• Common Table Expressions (CTEs)
• Date and string functions
• Window functions
• Ranking and running totals
• Customer segmentation queries
• Sales performance analysis
• KPI reporting queries
• Data quality validation queries
• Complete SQL business case study

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🎯 Goal

The goal of this repository is to strengthen SQL skills for Data Analytics and Business Intelligence while developing the ability to connect database queries with real business questions.

The progression is:

SQL SYNTAX
    ↓
DATA EXTRACTION
    ↓
DATA ANALYSIS
    ↓
BUSINESS METRICS
    ↓
INSIGHTS
    ↓
DECISION SUPPORT

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💡 Core Principle

Good SQL retrieves the right data. Good analytics explains why that data matters.