https://github.com/v6ntage/sql-sales_data-analytics-project
This repository contains a SQL scripts demonstration analytical techniques.
https://github.com/v6ntage/sql-sales_data-analytics-project
analytics business-analytics data data-analysis database query sql sql-server
Last synced: 6 months ago
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This repository contains a SQL scripts demonstration analytical techniques.
- Host: GitHub
- URL: https://github.com/v6ntage/sql-sales_data-analytics-project
- Owner: v6ntage
- Created: 2025-07-09T10:52:48.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2025-07-14T14:25:08.000Z (6 months ago)
- Last Synced: 2025-07-14T18:30:40.410Z (6 months ago)
- Topics: analytics, business-analytics, data, data-analysis, database, query, sql, sql-server
- Homepage: https://www.datascienceportfol.io/nastalekm
- Size: 5.35 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 📊 Sales Data Analytics Project
Contains collection of various analytical SQL scripts/queries for data exploration & analytics.
These scripts cover various analyses such as changes over time, cumulative, performance, data segmentation.
# 🧠 Project Overview
The goal of this project is to perform a detailed exploration of sales data to extract actionable insights related to:
- **Performance Analysis** (Year-over-Year, Month-over-Month)
- To measure the performance of products, customers, or regions over time.
- For benchmarking and identifying high-performing entities.
- To track yearly trends and growth.
- **Change Over Time Analysis**
- To track trends, growth and changes in key metrics over time.
- For time-series analysis and identifying seasonality.
- To measure growth or decline over specific periods
- **Data Segmentation Analysis**
- To group data into meaningful categories for targeted insights.
- For customer segmentation, product categorization, or regional analysis.
- **Cumulative Analysis**
- To track performance over time cumulatively.
- Calculate the total sales per month.
- Running total of sales over time.
# 🛠️ Technologies Used
- SQL & SSMS – for data extraction, cleaning, transformation, and analysis