This end-to-end Data Analytics project analyzes student academic performance and identifies the key factors influencing exam scores.
The project demonstrates the complete analytics workflow from data cleaning to dashboard creation.
• Microsoft Excel • Python • PostgreSQL • SQL • Power BI
Student-Analysis/
├── Dashboard/
│ └── Student_Performance_Dashboard.pbix # Power BI Dashboard file
├── Data/
│ └── student_analysis.xlsx # Raw and cleaned dataset
├── Screenshot/
│ ├── image1.png # Dashboard preview screenshot
│ └── image2.png # Dashboard preview screenshot
├── Scripts/
│ └── load_data.py # Python script for data processing
├── Sql/
│ └── quries.sql # SQL queries for analysis
├── Readme.md # Project documentation
└── requirement.txt # Python dependencies
| Folder | Purpose |
|---|---|
| Dashboard/ | Contains the Power BI dashboard file for visualization |
| Data/ | Stores the raw Excel file used for analysis |
| Screenshot/ | Contains preview images of the dashboard |
| Scripts/ | Python scripts for ETL and data processing |
| Sql/ | SQL queries for database operations and analysis |
Excel ↓ Python ↓ PostgreSQL ↓ SQL Analysis ↓ Power BI Dashboard
✔ Executive Summary
• Total Students • Average Exam Score • Pass Rate • Attendance • Study Hours
✔ Performance Drivers
• Teacher Quality • Attendance • School Type • Internet Access • Tutoring Sessions
(Executive Summary Screenshot)
(Performance Drivers Screenshot)
• Students with attendance above 85% achieved higher scores.
• High teacher quality positively influences academic performance.
• Students studying 31–40 hours/week achieved the highest scores.
• Internet access has a positive impact on performance.
✓ Excel Data Cleaning
✓ Python Automation
✓ PostgreSQL
✓ SQL
✓ Power BI
✓ Data Visualization
✓ KPI Design
✓ Dashboard Development
Abhishek Chaudhary
LinkedIn : https://www.linkedin.com/in/abhishek-chaudhary-py/
GitHub : https://github.com/dark-code77