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#Data Visualization Project

Overview

This project demonstrates how to analyze and visualize sales performance data using Excel (Pivot Tables & Charts) and Python (Matplotlib). The goal is to extract actionable business insights such as top-performing products, regional performance, and sales trends.

The project is beginner-friendly, portfolio-ready, and designed to mirror real-world data analysis workflows used by data analysts and database professionals.

Objectives

  • Analyze sales performance data
  • Identify top-performing products and regions
  • Apply sorting and Top 10 filtering techniques
  • Recreate Excel insights programmatically using Python
  • Visualize results using clean, professional charts

Dataset

  • File: Sales_Performance_100_Rows.xlsx

  • Records: 100 sales transactions

  • Key Fields:

    • Order ID
    • Product
    • Region
    • Sales Amount
    • Quantity Sold
    • Order Date

The dataset simulates a small-to-medium business sales environment.

Tools & Technologies

Excel

  • Pivot Tables
  • Pivot Charts
  • Sort (Largest → Smallest)
  • Top 10 Filters

Python

  • Python 3.x
  • Pandas
  • Matplotlib

Analysis Performed

1️⃣ Excel Analysis

Using Pivot Tables, the following insights were generated:

  • Total sales by Product
  • Total sales by Region
  • Sorted results from largest to smallest
  • Applied Top 10 filters to highlight best performers
  • Created column and bar charts for quick comparison

2️⃣ Python Visualization

The same insights were rebuilt using Python:

  • Loaded Excel data with Pandas
  • Aggregated sales by product and region
  • Sorted results programmatically
  • Visualized data using Matplotlib bar charts

This demonstrates how Excel-based insights can scale into automated analytics workflows.

Sample Visualizations

  • 📦 Top Products by Sales
  • 🌍 Sales by Region
  • 📈 Comparative Bar Charts

(All charts generated using Matplotlib with default styling for clarity.)

Project Structure

Data Visualization/
│
├── Sales_Performance_100_Rows.xlsx
├── sales_dashboard.py
├── README.md

How to Run the Python Script

  1. Navigate to the project folder:
cd "Data Visualization"
  1. Install dependencies:
pip install pandas matplotlib
  1. Run the script:
python sales_visualization.py

Key Takeaways

  • Excel Pivot Tables are powerful for quick insights
  • Python provides automation, scalability, and reproducibility
  • Sorting and Top-N analysis are essential business intelligence skills
  • Visual storytelling improves decision-making

Future Improvements

  • Add time-series sales trend analysis
  • Export charts automatically to image files
  • Integrate SQL for database-driven analytics
  • Build an interactive dashboard (Streamlit or Power BI)

Author

Eric Acha Database & Data Analytics Professional

##Project Demos Homepage Demo: ![Dashboard] (gifs/dashboard_imgee.gif)

Contact Page Demo: ![Dashboard Code] (gifs/dashboard_code.gif)

📌 This project is part of my data analytics and visualization portfolio.

⭐ If you found this project helpful, feel free to star the repository and connect with me on GitHub!