Excel-Data-Analysis

Overview

This repository showcases beginner-level data analysis projects using Microsoft Excel and Power BI. These projects demonstrate my growth in technical data analysis skills through hands-on practice.

The focus is on applying foundational techniques like data cleaning, organizing datasets, analyzing trends, and creating pivot tables to derive actionable insights. Additionally, I have integrated Power BI visualizations to enhance data storytelling and create interactive dashboards.

My goal is to demonstrate my progress, learn through experience, and showcase practical examples of Excel's and Power BI's capabilities in data analysis.

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Projects

Project: Walmart Dataset Analysis

Description:

Analyzed a Walmart sales dataset sourced from Kaggle. Although the dataset was originally clean, I intentionally introduced errors to practice data cleaning skills. The project focuses on answering specific business questions related to customer behavior.

Project Workflow:

1. Understand and Organize the Dataset:

• Structured columns for clarity and relevance.

• Identified the dataset's purpose based on business questions.

2. Data Cleaning:

• Addressed duplicates, inconsistencies, and incorrect formats.

• Standardized values, corrected typos, formatted dates, and handled missing data.

3. Pivot Tables and Insights:

• Investigate performance by regions and products.

• Explore key metrics to identify actionable insights.

• Created pivot tables answering business questions related to Customer Insights:

  • What is the average age and income of customers by loyalty level?

  • Which customer loyalty group spends the most?

  • How do payment methods vary among customer demographics?

• Applied the Scalper Method to generate concise, actionable insights.

4. Power BI Visualization

After completing the Excel analysis, I enhanced my insights by creating an interactive Power BI dashboard.

📊 Power BI Visualizations Created:

• KPI Card: Displays Total Customer Spending at a glance.

• Clustered Column Chart: Compares Average Income & Age by Loyalty Level.

• Pie Chart: Shows the spending distribution across loyalty levels.

• Stacked Bar Chart: Visualizes payment methods by customer age & gender.

• Pie Chart: Displays the overall payment method distribution.

By incorporating Power BI, I transformed static insights from Excel pivot tables into dynamic, interactive visuals that allow users to explore data trends efficiently.

5. Summarize Key Insights:

• Highlight key takeaways and actionable recommendations.

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Complete status:

✅ Data Cleaning Completed

✅ Customer Insights Pivot Tables (Excel) Completed

✅ Power BI Dashboard Completed

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Next Steps: I will expand this project to include:

Sales and Revenue Analysis:

• What is the total revenue generated?
• Which products or categories contribute the most revenue?
• How do sales vary across different store locations?

Demand & Forecasting Insights:

• Predicting trends using historical sales data.
• Identifying seasonality patterns.

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Folder Structure:

• Walmart_Data_Analysis/
  • raw_data/: Original Kaggle dataset (clean)
  • dirtied_data/: Modified dataset with introduced errors for practice
  • cleaned_data/: Cleaned dataset after applying data cleaning techniques
  • pivot_tables: Excel file containing pivot tables and insights (Customer Insights)
  • powerbi_dashboard/: Customer Insights Dashboard – Power BI (.pbix) containing interactive visualizations
  • README.md: Project details and key takeaways

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Why Excel?

Microsoft Excel is my starting point because of its:

1. Accessibility: Widely used across industries and available to most professionals.
2. Versatility: Offers tools like pivot tables, formulas, and visualization capabilities that are critical for basic data analysis.
3. Simplicity: An ideal platform for beginners to learn fundamental data analysis techniques.
4. Powerful Features: Includes pivot tables, conditional formatting and robust functions.

Why Power BI?

Power BI enhances my analysis by:

1. Creating interactive dashboards with drill-down capabilities.
2. Building custom visuals to enhance analysis.

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Tools Used

Microsoft Excel:

• Data cleaning and preparation.
• Basic formulas and functions for analysis.
• Pivot tables for deriving insights.

📊 Microsoft Power BI:

• Creating interactive dashboards
• Building custom visuals to enhance analysis
• Visualizations to present findings

About

This repository reflects my progress as I transition into the field of data analysis. It highlights:

• My structured approach to learning through hands-on projects.
• Real-world scenarios tackled using foundational tools.
• A commitment to continuous growth and development in technical skills.

Feel free to explore my projects, share feedback, or offer suggestions for improvement. Your insights are invaluable to my development as a data analyst.