WSQ AI Vibe Coding Courses

WSQ AI Vibe Coding Courses

Vibe coding is the practice of building working software by directing an AI coding assistant in natural language — describing the outcome, reviewing what the AI produces, then steering, testing and refining it. It has moved software development from typing every line to specifying, reviewing and iterating, and it lets developers ship far faster while opening real application development to people who are not full-time programmers.

Our WSQ AI Vibe Coding Courses teach this discipline across the languages and platforms professionals actually deliver on. Web and full-stack tracks cover professional web apps, React full-stack builds, ASP.NET, RESTful APIs and eCommerce stores. Mobile tracks cover iOS and Android. Data and AI tracks cover Python, SQL, data analytics, data mining and modeling, deep learning, PyTorch and image generation. Specialist tracks cover game development, UI/UX, blockchain and Web3, C#, and multi-agent systems.

Every course is hands-on and outcome-based: participants finish having built and run something real. Just as important, the courses teach the judgement that separates productive vibe coding from generated code nobody can maintain — how to specify precisely, review and test AI output, catch subtle defects and security issues, and keep architecture sound as the codebase grows.

What you will learn

  • Directing AI coding assistants effectively - prompting, specifying, iterating and reviewing
  • Building full-stack web applications, RESTful APIs and eCommerce stores with AI assistance
  • Developing iOS and Android mobile apps through AI-assisted workflows
  • Applying vibe coding to Python, SQL, data analytics, data mining, deep learning and PyTorch
  • Specialist builds: game development, UI/UX, blockchain and Web3, C#, and multi-agent systems
  • Reviewing, testing and securing AI-generated code so what you ship stays maintainable

Who should attend

Software developers and engineers who want to ship faster, data analysts and scientists who code as part of their work, technical product managers and designers building prototypes, IT professionals automating tasks, and career switchers who want to build real applications. Courses range from beginner-friendly to advanced.

Funding

These are WSQ, CASL and IBF funded courses. Eligible Singaporeans and PRs can enjoy SkillsFuture funding subsidies, and SkillsFuture Credit may be used to offset the nett course fee. Company-sponsored participants may also be eligible for SkillsFuture Enterprise Credit (SFEC) and absentee payroll. Please refer to each course page for the funding schemes that apply.

Items 21 to 22 of 22 total

per page

Page:
  1. 1
  2. 2
  • WSQ - AI Vibe Coding for Data Mining and Modeling

    • WSQ
    • SFC
    • PSEA
    • SFEC
    • Absentee Payroll
    • MCES
    5 Review(s)
    This course equips participants with practical skills to use AI vibe coding and Python for data mining, computational modelling, and insight generation. Learners will use natural-language instructions and AI coding assistants to generate, explain, test, debug, and refine code, enabling them to develop data solutions more efficiently without writing every component manually.Participants will learn how to collect, clea....
    $900.00 (GST-exclusive)
    $981.00 (GST-inclusive)

    before funding and GST

  • WSQ - AI Vibe Coding for Data Analytics

    • WSQ
    • SFC
    • PSEA
    • SFEC
    • Absentee Payroll
    • MCES
    27 Review(s)
    This course equips participants with practical skills to use AI vibe coding and Python for data analytics. Learners will use natural-language instructions and AI coding assistants to generate, explain, test, debug, and refine Python code, making data analysis faster and more accessible without requiring them to write every line of code manually.Participants will learn how to import, clean, transform, and organise dat....
    $750.00 (GST-exclusive)
    $817.50 (GST-inclusive)

    before funding and GST

Items 21 to 22 of 22 total

per page

Page:
  1. 1
  2. 2