A full-stack web application that simulates the complete lifecycle of a university admission process. It features live data generation, a transactional SQL backend, and real-time analytics via embedded Power BI.
(Fig 1: The Admin Dashboard showing real-time applicant metrics)
Unlike typical data analysis projects that rely on static, pre-existing datasets, this system creates its own data ecosystem:
- ✅ Dynamic Data Generation: Students submit real applications through the web interface, generating fresh data points instantly.
- ✅ End-to-End Workflow: Simulates the entire journey—from a student's submission to the database transaction, to the admin's decision-making process.
- ✅ Live Analytics: The embedded Power BI dashboard updates to reflect the latest SQL database state, enabling real-time decision-making.
The command center for university staff. Admins can visualize trends and process applications efficiently.
- Embedded Analytics: A fully interactive Power BI report integrated directly into the Flask application.
- Application Management: View, filter, and sort incoming student applications.
- One-Click Actions: Accept or Reject applicants, triggering immediate status updates in the SQL database.
- Program Management: Monitor active programs and departmental statistics.
A user-friendly interface for applicants to manage their admission journey.
- Secure Authentication: User registration and login functionality.
- Application Submission: Comprehensive forms capturing GPA, SAT scores, and Statements of Purpose (SOP).
- Real-Time Tracking: Students can see their application status change (Waitlisted → Accepted/Rejected) instantly after an admin decision.
- Profile Management: Record academic achievements, scholarships, and personal details.
The application uses a robust MVC (Model-View-Controller) architecture with two operational modes (SQL Server or CSV-based).
| Component | Technology | Description |
|---|---|---|
| Backend | Python, Flask | Handles routing, auth logic, and data processing. |
| Database | MS SQL Server | Relational storage with normalized schema & constraints. |
| Frontend | HTML5, Bootstrap 5 | Responsive UI for mobile and desktop. |
| Analytics | Microsoft Power BI | Embedded reports for data visualization. |
| Driver | PyODBC | Secure SQL connectivity with parameterized queries. |
The system relies on 7 normalized tables to ensure data integrity:
users ↔ applicants ↔ applications ↔ programs
(Plus supporting tables for achievements, academic profiles, and lookups)
Beyond being a web application, this project functions as a complete ETL (Extract, Transform, Load) pipeline that moves raw data into actionable insights.
The system sources data from two origins:
- User Input: Raw form data captured from the Flask frontend.
- Synthetic Simulation: A custom Python script (
generate_data.py) that acts as a data generator. It "extracts" probabilistic logic based on real-world Indian college trends (2017–2026) to create realistic applicant profiles.
Before storage, raw data undergoes significant transformation:
- Normalization: Flat CSV data is restructured into a 3NF (Third Normal Form) relational schema with 7 interconnected tables.
- Data Cleaning: The migration scripts handle date formatting, ID generation (e.g.,
APP9001), and null value handling. - Business Logic: Application statuses are calculated based on acceptance rates and seasonal spikes (e.g., March-April peaks).
- Operational Storage: Processed data is loaded into Microsoft SQL Server using transaction-safe operations to ensure integrity.
- Analytical Loading: The data is finally ingested by Power BI, which visualizes the "Load" stage through a live dashboard embedded back into the Admin Portal.
I have designed this application to run in two modes:
- SQL Server Mode (Recommended): Full production simulation.
- CSV Mode: Lightweight version for testing without a database server.
👉 Click here for the Detailed USAGE Guide Complete instructions on generating synthetic data, migrating to SQL, and configuring the Power BI dashboard.
- Cloud Deployment: Deploy database to Azure SQL and app to Azure App Service.
- Containerization: Dockerize the application for consistent deployment environments.
- Automation: Implement email notifications for status updates.
- CI/CD: Set up GitHub Actions for automated testing and deployment.
- Full-Stack Development: Integrating a Python/Flask backend with a responsive frontend.
- Database Design: Implementing normalization, foreign keys, and transaction integrity.
- Data Engineering: Building pipelines that move data from User Input → SQL Server → BI Tools.
- Security: Handling role-based access control (RBAC) and preventing SQL injection.
Educational project - MIT License