An end-to-end SQL Data Analytics project built using Microsoft SQL Server, demonstrating Exploratory Data Analysis (EDA) and Advanced SQL Analytics to extract meaningful business insights from sales data.
This project showcases how SQL can be used beyond querying data by performing real-world business analysis.
The project begins with Exploratory Data Analysis (EDA) to understand the dataset, validate data quality, and explore key business dimensions. It then progresses to Advanced SQL Analytics, where business questions are answered using advanced SQL techniques such as Window Functions, Common Table Expressions (CTEs), ranking functions, aggregations, and reporting queries.
The goal is to transform raw business data into actionable insights that support data-driven decision-making.
- Explore and understand the dataset
- Perform Exploratory Data Analysis (EDA)
- Analyze business dimensions and measures
- Identify trends and patterns
- Answer business questions using SQL
- Generate analytical reports
- Practice advanced SQL techniques used in Data Analytics
The project follows the following analytical workflow:
- Database Exploration
- Dimensions Exploration
- Date Exploration
- Measures Exploration
- Magnitude Analysis
- Ranking Analysis
- Change-over-Time Analysis
- Cumulative Analysis
- Performance Analysis
- Part-to-Whole Analysis
- Data Segmentation
- Reporting
The Exploratory Data Analysis phase focuses on understanding the dataset before performing business analysis.
Topics covered include:
- Database exploration
- Dimension exploration
- Date exploration
- Measures exploration
- Magnitude analysis
- Top N & Bottom N analysis
Example business questions:
- How many customers are in the dataset?
- How many products are available?
- Which categories generate the highest revenue?
- What is the sales distribution across different dimensions?
- Which products have the highest sales?
The Advanced Analytics phase focuses on answering real business questions using SQL.
Topics covered include:
- Change-over-Time Analysis
- Cumulative Analysis
- Performance Analysis
- Part-to-Whole Analysis
- Customer & Product Segmentation
- Business Reporting
Example business questions:
- How has sales changed over time?
- Which products contribute the highest revenue?
- Who are the top-performing customers?
- Which product categories perform best?
- What percentage does each category contribute to total revenue?
- Which customers belong to different spending segments?
This project demonstrates the use of:
- SELECT
- WHERE
- ORDER BY
- GROUP BY
- HAVING
- Aggregate Functions
- CASE WHEN
- Joins
- Common Table Expressions (CTEs)
- Window Functions
- Ranking Functions
- Subqueries
- Views
- Date Functions
- String Functions
- Mathematical Functions
The analysis provides insights into:
- Customer report (gold)
- Product report (gold)
- SQL
- Microsoft SQL Server
- Exploratory Data Analysis (EDA)
- Advanced SQL Analytics
- Data Analysis
- Window Functions
- Common Table Expressions (CTEs)
- Joins
- Aggregate Functions
- Ranking Functions
- Business Reporting
Through this project, I gained hands-on experience in:
- Exploring and understanding relational datasets
- Writing analytical SQL queries
- Solving business problems using SQL
- Applying Window Functions and CTEs
- Building business reports from raw data
- Performing real-world Exploratory Data Analysis