🎓 Student Grade Management System & Analytics

A full-stack data project that implements a CRUD (Create, Read, Update, Delete) system in Python to manage student records, persists data in a SQLite database, and performs statistical analysis and visualization using R.

📖 Project Overview

I built this project during my second semester in Information Systems to bridge the gap between application logic, database management, and data science.

The goal was to move beyond simple scripts and build a connected system:

1. Python acts as the frontend/backend, managing user input and database connections.

2. SQLite serves as the persistent storage layer.

3. R connects to that same database to perform statistical analysis (mean, distribution) and generate visualizations.

🛠️ Key Features

🐍 Python (The Application Layer)

• Complete CRUD Operations: Users can add, view, update, and delete student records via a console interface.

• Robust Input Validation:

  • Custom-written logic to validate email formats (checking for @ and .).

  • Grade validation (ensuring values are 0-100).

• Error Handling: Catches database connection errors to prevent crashes.

• SQL Injection Prevention: Uses parameterized queries (? placeholders) to ensure data security.

• User Experience: Nested menu loops allow users to perform multiple operations without restarting the program.

📊 R (The Analytics Layer)

• Direct Database Connection: Uses RSQLite to pull live data from the Python-generated database.
• Data Cleaning: Uses dplyr pipelines to categorize students (e.g., "Approved", "Excellent").
• Visualization: Generates histograms and boxplots using ggplot2 to analyze grade distributions.

🤝 The Development Process (Human + AI)

This project represents a "hybrid" learning approach.

• My Contribution (The Logic): I designed the application flow, wrote the custom validation algorithms (loops/conditionals), and built the user interface (CLI). I also implemented the "Update" logic to handle specific field changes.
• AI Mentorship (The Structure): I utilized AI to learn and understand the architectural best practices for connecting Python to SQLite and to learn the specific syntax for modern R libraries (dplyr/ggplot2).

🚀 How to Run

Prerequisites

• Python 3.x
• R and RStudio

1. Run the Python Application

python main.py

Follow the on-screen menu to create users and add grades.

2. Run the Analysis

Open analysis.R in RStudio or run via terminal:

Rscript analysis.R

This will generate statistical summaries and save a user_data.csv file.

📈 Future Improvements

• Add a graphical user interface (GUI).

• Add more advanced analytics.

• Improve Python logic with better error handling/organization.