🏅 Olympics Analytics Dashboard

An interactive Power BI project analyzing Olympic athlete performance, medals, and participation trends.
Built using MySQL + Excel + Power BI + DAX.

This project demonstrates data analysis, dashboard creation, and KPI computation to explore patterns in Olympic data.

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📊 Dashboards

1️⃣ Olympic Insights

KPIs:

• Total Athletes
• Total Medals
• Total Gold Medals

Charts / Visuals:

• Number of Athletes Over Years
• Top 4 Sports by Total Medals
• Total Athletes by Sex

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2️⃣ Olympic Medal Insights

KPIs:

• Total Gold Medals
• Total Silver Medals
• Total Bronze Medals
• Athletes with No Medal

Charts / Visuals:

• Total Medals Over Years
• Top 10 Countries by Total Medals
• Percentage of Athletes with Medals

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3️⃣ Athlete Performance

KPIs:

• Average Height
• Average Weight
• Average Age
• Average BMI

Charts / Visuals:

• Average Height & Weight by Age Group
• Top 5 Sports by Average Height
• Medals by Age Group

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⚙️ Tools Used

• Excel → Data cleaning, preparation, and computing derived metrics
• MySQL → Data storage, queries & preparation
• Power BI → Dashboard creation
• DAX → KPI calculations

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📥 Resources

• Power BI File (.pbix):
  👉 Download PBIX File
• SQL Queries:
  👉 View SQL Queries
• DAX Formulas:
  👉 View DAX Formulas
• Cleaned Excel Dataset (ready for analysis, 269,733 rows):
  👉 Download Excel File

Data Cleaning / Pipeline Notes:

• Original raw dataset contained missing values and inconsistent column names.
• Cleaned and processed in Excel:
  • Removed rows with missing Height, Weight, Age, or MedalWon.
  • Standardized text fields (Sport, Country, MedalWon).
  • Computed derived metrics:
    • BMI = Weight / (Height/100)^2
    • AgeGroup = <20, 20–24, 25–29, 30+
• Ensured numeric columns are properly formatted for analysis.
• Final cleaned dataset contains 269,733 rows ready for Power BI dashboards.

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🚀 Key Insights

• Athlete participation has increased steadily across Olympic years.
• USA, Russia, Germany dominate total medal counts.
• Physical metrics (BMI, Height, Weight) show clear patterns across sports.
• Age group 20–24 has the highest medal wins.
• Top-performing sports: Rugby, Lacrosse, Alpinism, Cricket.
• Male athletes outnumber female athletes across the dataset.
• Average athlete height and weight differ significantly by sport.

✦ Created as part of my Data Analytics Portfolio ✦