Excel Dashboard: Bicycle Purchases

This project demonstrates my ability to create an **Excel Dashboard** using a dataset of bicycle purchases. The dashboard visualizes key metrics based on **marital status**, **age**, and **income**. The analysis involved using pivot tables, charts, and slicers to explore how demographic factors influence bicycle purchases.

Project Overview

The Excel dashboard was designed to present actionable insights from the dataset, allowing users to interact with the data through slicers and pivot charts. Key features of the dashboard include:

• Pivot Tables and Charts: Used pivot tables to aggregate data by age, marital status, and income, with corresponding pivot charts to visualize trends.
• Slicers for Interactivity: Incorporated slicers to allow the user to filter data dynamically by age group, marital status, and income bracket.
• Key Insights: Identified purchasing patterns based on demographic variables, revealing key trends in bicycle purchases.

Link to GitHub Repository

You can access the full project, including the Excel file and related resources, on my GitHub repository. Click the link below to view the repository:

Skills Developed

Through this project, I developed and refined the following key **data analytics** skills:

• Excel Data Analysis: Proficient in using pivot tables, slicers, and pivot charts to summarize and visualize data in Excel.
• Data Visualization: Designed interactive dashboards that provide clear insights through various types of charts and filters.
• Dynamic Reporting: Leveraged Excel’s slicers and dynamic charts to make the dashboard interactive and user-friendly.
• Data Insights: Applied demographic analysis to uncover trends and patterns in bicycle purchasing behavior.

Conclusion

This Excel project demonstrates my ability to perform data analysis and visualization using Excel's powerful features. By creating an interactive dashboard, I was able to deliver valuable insights into how demographic factors influence bicycle purchases, and I look forward to applying these skills in other data-driven projects.