This project presents an interactive Power BI dashboard designed to analyze student academic performance. The analysis focuses on Math, Reading, and Writing scores while examining how different factors such as gender, parental education level, lunch type, and test preparation courses influence student outcomes.
The goal of this project is to transform raw data into meaningful insights using data visualization techniques.
The dataset used in this project contains information about student academic performance. It includes the following fields:
- Gender
- Race/Ethnicity
- Parental Level of Education
- Lunch Type
- Test Preparation Course
- Math Score
- Reading Score
- Writing Score
This dataset allows us to explore different factors that may influence student academic outcomes.
The following tools were used to complete this project:
- Power BI – Data visualization and dashboard creation
- Excel / CSV Dataset – Data source
- Basic DAX Measures – Used to calculate KPIs and averages
- GitHub – Project documentation and version control
The Power BI dashboard includes several interactive visualizations:
- KPI Cards showing average scores across subjects
- Bar Charts comparing performance by gender
- Column Charts analyzing parental education influence
- Pie Charts showing lunch distribution
- Scatter Plot exploring relationships between reading and writing scores
- Tables and matrices summarizing student performance
These visuals help transform raw data into easy-to-understand insights.
This dashboard was designed to answer several analytical questions about student academic performance:
- What is the overall average performance across Math, Reading, and Writing?
- Do male and female students perform differently across subjects?
- How does parental education level influence student academic performance?
- Does completing a test preparation course improve student scores?
- What relationship exists between reading and writing scores?
- Does lunch type (standard vs free/reduced) influence student performance?
- Which subject shows the highest average score among students?
- What patterns can be observed across different race/ethnicity groups?
These questions help guide the analysis and highlight key patterns within the data.
Some important insights from the analysis include:
- Students who completed the test preparation course tend to perform better on average.
- There is a strong correlation between reading and writing scores.
- Parental education level appears to influence student academic performance.
- Students with standard lunch generally perform better than those with free or reduced lunch.
This project demonstrates how Power BI can be used to transform raw educational data into meaningful insights through interactive dashboards.
By analyzing student performance across multiple factors, this dashboard highlights patterns that may help educators and stakeholders better understand academic outcomes.
The project also reflects my growing skills in data analysis, data visualization, and storytelling with data.
Possible future enhancements for this project include:
- Adding more advanced DAX calculations
- Expanding the dashboard with additional analytical pages
- Integrating additional datasets for broader analysis
- Publishing the dashboard on Power BI Service for online sharing
👤 Author Maduabuchi Goodness Chidera Aspiring Data Analyst