Advanced Undergraduate / Graduate

COMP466 R8: Advanced Technologies for Full Stack Web Developers

A comprehensive, systems‑oriented journey from foundations to production‑ready full‑stack applications
Fall / Spring 2026 3 credits (lecture + lab) Advanced Technologies for Full-Stack Web Application Development , by H. Wang Prerequisites: OOP, HTML/CSS, JS, DB, SE Syllabus

Course Overview

This course provides an advanced, hands‑on study of modern web‑based systems development from a full‑stack perspective. You will learn how to design, build, secure, deploy, monitor, and maintain contemporary web applications using industry‑standard technologies and practices. The curriculum follows a systems‑oriented path—starting with foundational web architectures, then progressing through frontend development, data management, backend engineering, system design, DevOps, cloud deployment, performance optimization, testing, monitoring, and emerging trends.

Based on the OER textbook Advanced Technologies for Full-Stack Web Application Development, by H. Wang, this course is designed for the AI era and emphasizes practical skill development through hands-on laboratories, programming exercises, homework, mini-projects, case studies, module projects, and a comprehensive capstone project. By the end of the course, you will have designed, built, tested, and deployed a production-ready full-stack web application while developing in-depth expertise in modern full-stack technologies and practices.

Prerequisites

Required:

Recommended: Computer Networks, Linux Command Line, Version Control (Git).

You are expected to have proficiency in at least one programming language, familiarity with HTML/CSS, command‑line usage, and basic database knowledge.

Learning Outcomes

Analyze modern full‑stack system architectures.
Design frontend applications using component‑based frameworks (React).
Develop backend services with Node.js/Express or equivalent.
Design and implement relational and NoSQL data architectures.
Build and integrate RESTful APIs and real‑time systems.
Implement authentication and authorization solutions.
Apply professional software engineering and DevOps practices (Git, CI/CD, Docker).
Deploy and manage applications in cloud environments (AWS, Azure, or GCP).
Monitor, test, secure, and optimize production systems.
Design, implement, and evaluate a complete enterprise‑scale web application.

Study Guide & Recommended Pacing

Suggested weekly schedule (14‑week semester): Each module corresponds to roughly one week, with extra time for the capstone and final assessment.

Week Module Focus Assessments
1 Module 1 Foundations of Web Applications
2 Module 2 Frontend Development (React) Assignment 1 starts
(due Week 3)
3 Module 3 Data Management (SQL, NoSQL, ORM) Assignment 1 due
4 Module 4 Backend Development (Node.js, Auth) Assignment 2 starts
(due Week 6)
5 Module 5 System Design, Git, Docker, CI/CD, Cloud
6 Module 6 Advanced Topics (Perf, Real‑time, Monitoring, Testing) Assignment 2 due
7–9 Module 7 Capstone Project – Design & Implementation Capstone Project starts (Week 7)
Assignment 3 starts (Week 7)
(Assignment 3 due Week 9; Capstone due Week 13)
10–11 Module 8 Backend Specialization Tracks (choose one) Assignment 4 starts (Week 10)
(due Week 12)
12–13 Module 9 Review, Practice Exams, Final Project & Portfolio Assignment 4 due (Week 12)
Capstone Project due (Week 13)
14 Comprehensive Final Examination Final Exam

Each module includes tutorials, labs, exercises, homework, mini‑projects, case studies, and a module project (where applicable). The final exam is scheduled for Week 14.

Assessment & Assignments

Assessment Weight Due Week
Assignment 1: Frontend Foundations 12% Week 3
Assignment 2: Data Layer & Backend APIs 12% Week 6
Assignment 3: DevOps, CI/CD & Cloud Deployment 12% Week 9
Assignment 4: Backend Specialization PoC 12% Week 12
Capstone Project: Full-Stack Application 37% Week 13
Final Exam: Comprehensive 15% Week 13
Total 100%

For detailed instructions, marking rubrics, and submission guidelines, please refer to the respective assignment pages linked in the course modules.

Course AI Use Policy

Our Guiding Principle: AI is a partner, not a substitute. You are encouraged to use AI to accelerate your learning, brainstorm ideas, debug code, and explore alternative solutions. However, you remain fully accountable for every piece of work you submit.

Permitted Use: You are permitted to use AI tools for learning, concept clarification, code generation, debugging, testing, and documentation drafting.

Mandatory Requirements:

Restrictions: AI tools are not permitted during the final exam. Submitting AI-generated work without substantial personal contribution, or failing to document AI use, will be treated as academic misconduct.

Final Exam

Format: A 3-hour, closed-book, proctored exam consisting of four sections:

Scope: The exam covers all core modules (1-7). Sample questions are available in the course materials.

Important: AI tools and other unauthorized aids are strictly prohibited during the exam.

Modules at a Glance

Assignment Submission Guidelines

All assignments, projects, and the capstone are submitted via the LMS. Follow these steps for every assessment:

  1. Read the assignment brief – Detailed instructions and rubrics are available on the dedicated assignment pages linked from each module.
  2. Prepare your deliverables – Ensure you have completed all required files (code, documentation, logs).
  3. Push to GitHub – Commit and push your final code to your public GitHub repository.
  4. Deploy (if applicable) – For assignments requiring deployment, ensure your live URL is working.
  5. Submit via LMS – Use the submission link provided in the module to upload your deliverables (GitHub URL, deployment URL, PDFs).
  6. Confirm submission – Double‑check that all files are attached and the submission is successful.

Important: Late submissions are subject to a 10% penalty per day (up to 5 days). Extensions must be requested before the deadline with a valid reason. All work must be your own; AI use must be documented in your AI Interaction Log.