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International Computer Science Accelerator Program chính là lộ trình đào tạo lý tưởng để bạn bứt phá trong 2 năm trước khi bước vào môi trường học tập quốc tế!
Chương trình đào tạo 2 năm IT & English toàn diện dành cho học sinh chuẩn bị du học ngành Công nghệ Thông tin.
- Bạn đang là học sinh cấp 2 hoặc cấp 3 và có ước mơ du học chuyên ngành Computer Science tại các quốc gia phát triển như Mỹ, Canada, Úc, Anh, Singapore, Nhật Bản...?
- Bạn muốn chuẩn bị nền tảng vững chắc về chuyên môn Công nghệ Thông tin giúp bạn tự tin khi đi du học?
👉 International Computer Science Accelerator Program chính là lộ trình đào tạo lý tưởng để bạn bứt phá trong 2 năm trước khi bước vào môi trường học tập quốc tế!
The International Computer Science Accelerator Program is a 10-month intensive training pathway designed for students preparing to study abroad in Computer Science, Software Engineering, or Data & AI–related majors. The program focuses entirely on core IT competencies, providing a strong foundation and practical skills through hands-on learning and real-world projects.
Across four structured semesters, learners build comprehensive capabilities in:
• Full-Stack Web Development (HTML, CSS, JavaScript, Node.js, React.js)
• Python Web Development (Django / Flask)
• Mobile Application Development (Flutter)
• Data Science & AI (Python, R, Machine Learning, Deep Learning)
• AI for Automation (AI Agent N8N, Microsoft Power Automate)
Each semester is divided into specialized modules, featuring:
• Real case studies from enterprises
• Weekly practical exercises
• Mini-Projects at the end of every semester
• An applied learning model aligned with the expectations of international colleges and universities
The program is optimized for students aiming to:
• Build a strong academic foundation before entering international CS programs
• Develop a competitive portfolio for university applications
• Gain practical IT experience comparable to first-year CS students abroad
• Explore multiple fields in tech to shape their future academic major
With a balance of theory, hands-on practice, and project-based learning, the program equips learners with essential skills, technical confidence, and a global mindset—ensuring they are fully prepared for international academic environments and future tech careers.
Upon completing the International Computer Science Accelerator Program, learners will be able to:
- Understand core CS concepts including data structures, algorithms, networking, and software architecture.
- Apply computational thinking to analyze and solve technical problems effectively.
- Use industry-standard tools such as Git, GitHub, IDEs, and project management workflows.
- Develop responsive and interactive web interfaces using HTML, CSS, JavaScript, and React.js.
- Design and implement backend systems using Node.js, Express, and database technologies.
- Integrate frontend and backend components to build complete full-stack applications.
- Deploy web applications to cloud platforms.
- Build dynamic web systems using Django or Flask.
- Work with ORM, API development, authentication, and middleware.
- Design scalable web architectures following international best practices.
- Use Flutter & Dart to develop mobile applications for iOS and Android.
- Build UI/UX flows, integrate APIs, manage states, and deploy apps.
- Use Python & R for data exploration, data cleaning, visualization, and statistical analysis.
- Build, train, and evaluate Machine Learning and Deep Learning models.
- Apply AI techniques in classification, regression, clustering, NLP, and Computer Vision.
- Interpret and present insights through dashboards, reports, and visualizations.
- Build automation workflows using AI Agent N8N and Microsoft Power Automate.
- Integrate APIs, webhooks, and AI models into automated business processes.
- Design real-world automation systems aligned with enterprise needs.
- Apply Agile/Scrum methodologies in team-based development.
- Use version control, coding standards, and collaborative workflows.
- Build Mini-Projects every semester and complete a capstone portfolio-ready project.
- Demonstrate problem-solving, communication, documentation, and presentation skills.
- Build a portfolio of web, mobile, data science, and AI projects aligned with global education standards.
- Prepare fully for university applications: personal statements, project showcase, GitHub profile, and technical interviews.
- Gain confidence to join Computer Science programs abroad at an advanced level.
To ensure students can follow the program effectively, learners should meet the following prerequisites:
- Completed grade 9 or higher (for high-school students), or
- Currently studying in high school/college interested in Computer Science.
- Ability to use a computer confidently (Windows or macOS).
- Familiarity with basic software: web browser, file management, typing skills.
- Ability to install software following instructions.
- Basic English reading comprehension (A2 → B1 level).
- Ability to understand simple technical instructions in English.
- Note: No advanced English is required; technical English will be improved throughout the program.
- Curiosity and passion for technology, programming, and digital tools.
- Willingness to learn by doing through projects and case studies.
- Commitment to follow at least 10 months of intensive learning.
- CPU: Intel i5 / Ryzen 5 or higher
- RAM: Minimum 8GB (recommended 16GB for Data Science & AI)
- Storage: 256GB SSD or higher
- Stable internet connection
- Students do not need to know coding beforehand.
- The program starts from fundamentals and progresses to advanced topics.
To successfully complete the program and apply the acquired knowledge in real-world projects, students are expected to meet the following requirements:
- Attend at least 80% of classes in each module.
- Actively participate in exercises, group work, and in-class coding sessions.
- Maintain consistent study habits for a minimum of 6–8 hours of self-study per week.
- Finish weekly coding tasks and quizzes.
- Complete all case studies, lab exercises, and mini-projects at the end of each semester.
- Submit assignments on time via LMS/GitHub.
- Push code regularly to GitHub.
- Maintain clean, readable, well-documented code.
- Organize projects clearly to serve as part of a university application portfolio.
- By the end of the program, learners are expected to:
- Understand fundamental programming concepts and be able to write functional programs independently.
- Build at least 1–2 working applications per semester (web, mobile, Data/AI).
- Apply learned tools (Python, React, Flutter, Django, Data Science, Automation AI) in real mini-projects.
- Communicate effectively in English during presentations and discussions.
- Collaborate in group projects using Agile/Scrum or similar teamwork methodologies.
- Practice problem-solving and debugging independently.
- Successfully complete a capstone project that demonstrates mastery in at least one focus area:
• Web Development
• Mobile Development
• Data Science & AI
• AI Automation
- Present the final project professionally (slides, demo, documentation).
- Laptop is maintained in good condition and software required for modules is installed properly.
- Ability to perform troubleshooting with support from instructors when needed.
| STT | Module Name | Knowledge |
|---|---|---|
| 1 |
Module 1: CS Foundations & Git/GitHub(12 hours) |
Detailed Content • Computer Systems, OS, CPU, Memory, I/O • Basic Networking: IP, Domain, DNS, HTTP/HTTPS • Software Development Process: Agile, Scrum • Basic Git: init, clone, commit, push • Advanced Git: branch, merge, pull request Case Study • Team collaboration using one shared GitHub repository. Learning Outcome • Ability to work with Git/GitHub like an international developer. |
| 2 |
Module 2: HTML & CSS (24 hours) |
Detailed Content • HTML page structure • Semantic HTML5 • CSS basics: selectors, box model • Advanced CSS: Flexbox, Grid • Responsive Web Design • Basic UI/UX fundamentals • Converting Figma designs into HTML/CSS Case Study • Clone UI layouts: Shopee / Starbucks / Tiki. Learning Outcome • A fully responsive landing page. |
| 3 |
Module 3: JavaScript ES6+ (24 hours) |
Detailed Content • ES6 features: let/const, arrow functions, spread, destructuring • DOM manipulation • Event handling • AJAX & Fetch API • JSON • LocalStorage Case Study • To-Do List / Notes App / Shopping Cart Web App. Learning Outcome • Frontend web application with full CRUD functionality. |
| 4 |
Module 4: ReactJS (36 hours) |
Detailed Content • SPA concepts • Component lifecycle • Props & State • Hooks (useState, useEffect, useContext) • Routing with React Router • State management: Context API / Redux • API Integration • UI Libraries: Material UI, Ant Design Case Study • Product Management Dashboard (CRUD + Filter + Search). Learning Outcome • A complete React application connected to a real API. |
| 5 |
Module 5: NodeJS & Express (12 hours) |
Detailed Content • NodeJS runtime environment • Express routing • REST API development • Database connection (MongoDB/MySQL) • JWT Authentication • Error handling & middleware Case Study • Authentication API (Sign up / Login) + Product CRUD. Learning Outcome • Backend API ready for frontend consumption. |
| 6 |
⭐ Mini Project (Full-Stack) — 12 hours |
• Students build one complete system: • E-commerce Mini App • Booking System • Social Network Mini App |
| STT | Module Name | Knowledge |
|---|---|---|
| 1 | Module 1: Python Foundations — 18 hours | Detailed Content • Variables, functions, loops, conditionals • OOP: class, object, inheritance, polymorphism • Exception handling • File I/O • Data structures: List, Dict, Tuple, Set • Virtual environments Case Study • HR Management System using Python OOP. Learning Outcome • Ability to use Python to solve real-world business logic. |
| 2 |
Module 2: Django — 36 hours |
Detailed Content • Django project structure • MVT architecture • Models & ORM • Templates & Forms • Authentication & Authorization • Django Admin customization • Django REST Framework (API) • File upload, pagination, filtering • Email + background tasks Case Study • Blog Management System • Appointment Booking System Learning Outcome • Web application from zero to production-ready. |
| 3 |
Module 3: Flask — 24 hours |
Detailed Content • Flask routing • Jinja2 templates • Blueprint structure • REST API • JWT authentication • Database integration (SQLite/PostgreSQL) Case Study • Booking or Healthcare API system. Learning Outcome • Ability to build lightweight and high-performance APIs. |
| 4 |
Module 4: Database & Cloud Deployment — 18 hours |
Detailed Content • PostgreSQL / MySQL fundamentals • SQL queries (Join, Index, Aggregation) • Docker basics • Deploy Django/Flask to AWS / Render / Railway • Environment variables, DNS, HTTPS/SSL Case Study • Deploy a Python web app to a real cloud service. Learning Outcome • Practical deployment skills for modern applications. |
| 5 |
⭐ Mini Project — 24 hours |
• Social Network API • E-commerce Backend • Online Learning Management System API |
| STT | Module Name | Knowledge |
|---|---|---|
| 1 | Module 1: Flutter — 36 hours | Detailed Content • Dart fundamentals • Widget tree & layouts • Navigation • State management: Provider / Bloc • API Integration • Local data storage • Responsive UI Case Study • Notes app / Task manager / Ride-booking mini app. Learning Outcome • Cross-platform mobile app for Android & iOS. |
| 2 |
Module 2: Python for Data Analysis — 24 hours |
Detailed Content • Numpy & Pandas • Data cleaning • Data transformation • Data visualization (Matplotlib, Seaborn) Case Study • Sales performance analysis across 12 months. Learning Outcome • Complete data analysis report. |
| 3 |
Module 3: R for Analytics — 24 hours |
Detailed Content • R language basics • dplyr, tidyr • ggplot2 visualization • Statistical testing • Regression modeling Case Study • House price prediction using regression models. Learning Outcome • Professional-level data analytics report. |
| 4 |
Module 4: Machine Learning — 24 hours |
Detailed Content • ML workflow • Train/Test split • Classification (KNN, SVM, Decision Tree) • Regression (Linear, Polynomial) • Clustering (K-Means) • Model evaluation techniques Case Study • Customer segmentation • Churn prediction Learning Outcome • Build, evaluate and present ML models. |
| 5 |
⭐ Mini Project — 12 hours |
• Recommendation System • Forecasting Model • Classification Model |
| STT | Module Name | Knowledge |
|---|---|---|
| 1 | Module 1: AI for Automation (N8N, Power Automate) — 30 hours | Detailed Content • Workflow Automation concepts • Triggers & Actions • API Integration • Build custom AI Agents • Power Automate Cloud • PowerApps Forms • Automating email, approvals, and reporting Case Study • Automated approval workflow • Daily automated reporting system |
| 2 |
Module 2: Applied AI — 30 hours |
Detailed Content • NLP fundamentals • Text classification • Sentiment analysis • Computer Vision basics • OCR pipeline • Generative AI: LLMs, Embeddings, RAG • Vector databases (Chroma/FAISS) Case Study • AI Chatbot • Invoice OCR + data extraction |
| 3 |
Module 3: System Design — 24 hours |
Detailed Content • Scalability • API Gateway design • Caching layers • Load balancing • Microservices • Database sharding • High availability architecture Case Study • Design a system similar to Grab/Uber/Amazon |
| 4 |
Module 4: Portfolio & Interview Preparation — 12 hours |
Detailed Content • Building a professional portfolio website • Optimizing GitHub profile • International-standard resume (US/UK/CA/AUS) • Technical interview preparation • Mock interview sessions |
| 5 | ⭐ Capstone Project — 24 hours | Students select one track: • Option 1: AI Automation Platform Web App + Database + N8N + AI Agent • Option 2: AI-powered Mobile App Flutter App + ML Model • Option 3: Data Analytics Platform Dashboard + Forecasting + Automation Final presentation to an evaluation panel. |
