A structured SQL project focused on data exploration, business metrics, trend analysis, and segmentation, designed to simulate real-world Data Analyst workflows in a Business Intelligence environment.
This project demonstrates how SQL can transform raw transactional data into actionable business insights.
The repository contains structured SQL scripts used to analyze a retail sales dataset, focusing on common analytical tasks performed by Data Analysts.
The goal of this project is to showcase core SQL skills used in real business analytics environments, including data exploration, KPI generation, trend analysis, and product performance evaluation.
The roadmap below outlines the analytical workflow followed in this project.
sql-data-analytics-project/
│
├── docs/
│ └── project-roadmap.png # Visual project workflow
│
├── datasets/ # Raw datasets used for analysis
│
├── scripts/
│ ├── database_exploration.sql # Schema discovery & data profiling
│ ├── measures_and_metrics.sql # KPIs, revenue & sales metrics
│ ├── time_trend_analysis.sql # Monthly & YoY trend analysis
│ ├── cumulative_analysis.sql # Running totals & moving averages
│ ├── segmentation_analysis.sql # Customer & category segmentation
│ └── product_performance.sql # Product performance insights
│
└── README.md
Each script represents a real-world analytics task commonly performed by Data Analysts.
| # | Analysis | Description |
|---|---|---|
| 1 | Database Exploration | Schema discovery, table relationships, and data profiling |
| 2 | Measures & Metrics | Sales KPIs, revenue calculations, and business metrics |
| 3 | Time-Based Trends | Monthly performance analysis and year-over-year comparisons |
| 4 | Cumulative Analytics | Running totals, moving averages, and growth tracking |
| 5 | Segmentation Analysis | Customer segmentation and category-level insights |
| 6 | Product Performance | Identifying top and underperforming products |
-- Joins → INNER JOIN, LEFT JOIN
-- Aggregations → SUM(), AVG(), COUNT(), MIN(), MAX()
-- Window Functions → LAG(), LEAD(), AVG() OVER(), SUM() OVER()
-- Grouping → GROUP BY, HAVING
-- Filtering → WHERE, BETWEEN, IN, CASE WHEN
-- Query Design → CTEs, Subqueries, Query Structuring
-- Date Functions → DATEPART(), DATETRUNC(), FORMAT()These SQL techniques are commonly used in data analytics, reporting, and business intelligence workflows.
| Tool | Purpose |
|---|---|
| SQL Server (T-SQL) | Writing and executing analytical queries |
| SQL Server Management Studio (SSMS) | Database management and query testing |
| Microsoft Excel | Exporting and validating analytical results |
| Git & GitHub | Version control and project documentation |
This project highlights key Data Analyst competencies, including:
- Writing clean, efficient SQL queries
- Translating business questions into analytical queries
- Performing exploratory data analysis
- Building KPIs and business metrics
- Identifying trends and patterns
- Applying data segmentation techniques
- Structuring analytical workflows using SQL
Akash Saha — Aspiring Data Analyst with 4.5+ years of experience in fraud investigation, KYC compliance, and operational analytics at Amazon and Wipro.
Currently building end-to-end analytics projects using SQL, Excel, and Power BI to transition into a full-time Data Analyst role.
🔗 Connect with me:
This project is licensed under the MIT License — free to use, modify, and distribute with proper attribution.
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