https://github.com/parthkumarmpatel/sql-exploratory-data-analysis
SQL EDA scripts for sales data warehouse — metrics, insights, and rankings from my data warehouse project.
https://github.com/parthkumarmpatel/sql-exploratory-data-analysis
data-analysis exploratory-data-analysis sql-server
Last synced: 7 months ago
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SQL EDA scripts for sales data warehouse — metrics, insights, and rankings from my data warehouse project.
- Host: GitHub
- URL: https://github.com/parthkumarmpatel/sql-exploratory-data-analysis
- Owner: parthkumarmpatel
- License: mit
- Created: 2025-06-22T05:44:22.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-06-22T05:51:34.000Z (7 months ago)
- Last Synced: 2025-06-22T06:30:03.026Z (7 months ago)
- Topics: data-analysis, exploratory-data-analysis, sql-server
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📊 SQL EDA on Data Warehouse Project
This repository contains **SQL scripts** to perform **Exploratory Data Analysis (EDA)** on a **sales data warehouse**. The analysis is part of my **Data Warehouse Project**, and you can find the full project here:
👉 [Data Warehouse Project Link](https://github.com/parthkumarmpatel/SQL-Data-Warehouse)
👉 [Data Analyst Portfolio Project Link](https://github.com/parthkumarmpatel/SQL-Data-Analyst-Portfolio-)
---
## 🚀 What’s inside
The SQL scripts help to:
- 🔍 Explore the database structure (tables, columns)
- 👥 Profile customers (age, country, gender)
- 📦 Analyze products (categories, subcategories)
- 💰 Calculate key business metrics:
- Total sales and quantity sold
- Average selling price
- Total orders, products, and customers
- 📈 Generate dimension-based reports (by country, category, gender)
- 🏆 Identify top and bottom performers:
- Top customers by revenue
- Best/worst selling products
---
## 🗂 Structure
The SQL code covers:
- Database and table exploration
- Summary statistics
- Dimension-level insights
- Ranking with aggregation and window functions
---
## 📌 How to use
✅ Run the scripts in your SQL environment (SQL Server, Snowflake, BigQuery, etc.) connected to the data warehouse.
✅ Modify table or schema names (`Gold.fact_sales`, `Gold.dim_customers`, etc.) if needed for your environment.
---
## 🔗 Project link
👉 [Data Warehouse Project Link](https://github.com/parthkumarmpatel/SQL-Data-Warehouse)
---
## 💡 Author
**Parth Patel**
Feel free to connect on [LinkedIn](https://www.linkedin.com/in/parthkumar-patel21)