Advanced SQL Data Warehouse & Business Analytics

Project Description

Developed a data analysis project using Python to explore and analyze Superstore sales data, uncover business insights, and evaluate sales performance through data cleaning, exploratory data analysis, and visualization.

The project involved processing the original dataset, handling data quality issues, analyzing key business performance indicators, and creating visualizations to identify trends and patterns in sales and profitability.

Key Features

Data Cleaning and Preparation: Cleaned and prepared the raw Superstore dataset for accurate and reliable analysis.

Exploratory Data Analysis (EDA): Investigated sales data to identify patterns, trends, and relationships across different business dimensions.

KPI Analysis: Evaluated key performance indicators, including total sales, profit, and other relevant business metrics.

Data Visualization: Created charts to communicate findings and highlight important trends in business performance.

Business Insights: Analyzed product and sales performance to support better understanding of business results.

Data Export: Exported the cleaned dataset for further analysis and reporting.

Tools & Technologies

Python

Pandas

NumPy

Matplotlib and Seaborn

Jupyter Notebook

Visual Studio Code

Excel

Project Objective

The objective of this project was to transform raw sales data into meaningful insights through Python-based data analysis. It demonstrates practical skills in data preparation, exploratory analysis, KPI calculation, data visualization, and communicating findings to support data-driven decision-making.