SQL Advanced Data Analytics – Exploratory Data Analysis

This repository contains a complete SQL-based Exploratory Data Analysis (EDA) project developed in SQL Server. It demonstrates how SQL alone can be used to explore and analyze data in depth—without relying on external tools.

The project is organized as a set of individual scripts that focus on specific analytical techniques. Each script can be run on its own or combined for a full EDA workflow.

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🎯 Project Goal

The main goal of this project was to learn and showcase how far SQL can go in data analytics and EDA tasks. Instead of exporting data to Python or R, all exploration and analysis happens inside SQL Server using carefully designed queries.

This project is useful for:

• Data analysts looking to deepen SQL skills.
• Hiring managers or recruiters wanting to see real-world SQL applied to analytics.
• Teams interested in SQL-first analytics workflows.

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🗃 Database Overview

This project uses a simple but realistic database with four core tables:

Table Name	Description
Customers	Stores customer details such as CustomerID, Name, Contact Info, and Location. Useful for segmentation, demographics, and purchase behavior analysis.
Employees	Holds employee information such as EmployeeID, Name, Department, and Role. Used for linking orders to employees or analyzing team performance.
Orders	Contains order-level data including OrderID, CustomerID, EmployeeID, ProductID, Quantity, Price, OrderDate, and Status. This is the main fact table for trends, rankings, and cumulative analysis.
Products	Lists product details such as ProductID, ProductName, Category, and UnitPrice. Useful for product-level performance, part-to-whole analysis, and ranking.

All analyses in the project are performed using these four tables. You can adapt the queries to any similar schema by changing the table and column names.

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📂 Project Structure

The project follows a logical EDA flow. Each SQL file corresponds to a specific analysis area:

Script No.	Topic/Focus Area	Description
01	Database Exploration	Basic database information, table structures, and column profiling.
02	Dimensions Exploration	Explore categorical and dimensional data fields (Customers, Products, Employees).
03	Date Exploration	Work with dates in Orders: distributions, gaps, trends over time.
04	Measures Exploration	Explore numeric measures such as quantity, price, and revenue.
05	Big Numbers / Magnitudes	Identify large values, totals, and aggregations in sales and orders.
06	Ranking (Top N / Bottom N)	Rank customers, employees, or products based on sales or orders.
07	Change-Over-Time Trends	Track changes in key metrics over time using Orders.
08	Cumulative Analysis	Running totals of sales, cumulative orders, progressive metrics.
09	Performance Analysis	Compare employee, product, or customer performance.
10	Part-to-Whole Proportional Analysis	Ratios, percentages, and contribution analysis (e.g., each product’s share of total sales).
11	Data Segmentation	Group customers or orders into meaningful segments (by region, category, etc.).
12	Reporting Views	Combine outputs into reporting-friendly views or tables.

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🛠 How to Use

1. Set up the database

   • Create the four tables (Customers, Employees, Orders, Products).
   • Insert your data into each table.
   • Update connection details in SQL Server Management Studio (SSMS).

2. Run the scripts

   • Scripts are numbered from 01 to 12.
   • Open each .sql file in SSMS and run it against your database.
   • Check the output results directly in the Results pane.

3. Adapt to your data

   • Replace table names, column names, or database references to fit your dataset.
   • You can use the same logic on any SQL Server database.

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📝 Example Query

Here’s an example of one of the ranking techniques used in the project:

SELECT 
    CustomerID,
    SUM(Quantity * UnitPrice) AS TotalSales,
    RANK() OVER (ORDER BY SUM(Quantity * UnitPrice) DESC) AS SalesRank
FROM Orders o
JOIN Customers c ON o.CustomerID = c.CustomerID
GROUP BY CustomerID;


This gives a quick view of top customers by sales.

🗂 Folder Contents
SQL_Advanced_Data_Analytics/
│
├── 01_Database_Exploration.sql
├── 02_Dimensions_Exploration.sql
├── 03_Date_Exploration.sql
├── 04_Measures_Exploration.sql
├── 05_Big_Numbers.sql
├── 06_Ranking_Top_Bottom.sql
├── 07_Change_Over_Time_Trends.sql
├── 08_Cumulative_Analysis.sql
├── 09_Performance_Analysis.sql
├── 10_Part_to_Whole_Proportion.sql
├── 11_Data_Segmentation.sql
└── 12_Reporting.sql


Each file contains comments explaining the logic of the queries.

Source Credit - Data with Baraa YT channel.