Full Stack Development

• Task Management Web Application: Students will develop a responsive web-based task management system using Angular for the front end and Node.js with a database for the back end. Users can create, update, delete, and categorize tasks, as well as set deadlines and mark tasks as complete. This project allows students to practice full CRUD operations, user authentication, and UI design principles while building a practical productivity tool.

• Real-Time Chat Application: Students will create a real-time chat application utilizing WebSocket technology (e.g., Socket.io) integrated with a Node.js backend and Angular frontend. The application will allow users to join chat rooms, exchange instant messages, and view online user status. This project enables students to apply asynchronous programming concepts and real-time data exchange while managing user sessions and chat persistence in a database.

• Event Registration Platform: Students will develop a web-based event registration system where users can browse upcoming events, register, and receive email confirmations. Administrators can create, edit, and manage events. The system will employ Angular for the frontend, Node.js for backend APIs, and database storage for event and registration data. This project reinforces CRUD operations, user authentication, and email integration using third-party APIs.

• Expense Tracking and Budgeting App: Students will build a web application that allows users to input income and expenses, categorize transactions, and generate visual reports using charting libraries (e.g., Chart.js). The system will use Angular for the interface, Node.js for server-side processing, and a database to store financial data. This project helps students integrate data visualization with full-stack development while managing user data securely.

Machine Learning & AI examples:

• Data Classification for Customer Segmentation: Students will analyze a publicly available customer dataset using machine learning algorithms (e.g., decision trees, k-nearest neighbors) to classify customers into distinct segments based on behavior and demographic data. This could help a company improve targeted marketing efforts and customer service personalization.

• Artificial Neural Networks for Predictive Analytics: Students will examine and implement artificial neural networks (ANNs) on a dataset of sales transactions. They will build a model to predict future sales trends, helping the company optimize inventory management and forecast demand with more accuracy.

• Building a Recommendation System: Students will apply software development methodologies to create a recommendation engine using collaborative filtering or content-based filtering techniques. This application could be used by an e-commerce company to suggest products to users based on their browsing history or preferences, improving user engagement and sales.

• Data Regression for Price Optimization: Students will implement data regression algorithms (e.g., linear regression or support vector regression) on publicly available real estate pricing data. The model could be used by a company to predict property prices based on various factors like location, square footage, and market conditions, enabling better pricing strategies for real estate agents or property developers.