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Course Details
Course Details
What You'll Learn
Topic 1: Getting Started with AI Vibe Coding for Financial Analysis
Topic 2: Analysing Financial Data with AI
Topic 3: Portfolio and Investment Analysis with Vibe Coding
Topic 4: Building Financial Dashboards and Reports
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- Python 3 – free
- Visual Studio Code – free
- Google Colab – free, runs in the browser with a Google account
- An AI coding assistant with a free tier, e.g. GitHub Copilot Free in Visual Studio Code
Hardware: Windows PC or Mac laptop
How to Apply HRD Corp Funding
This course is HRD Corp claimable for employers registered with HRD Corp, under the HRD Corp Claimable Courses scheme (SBL-Khas). Your company applies for the grant before the class; we submit the training provider claim after it.
- Check your levy balance - your company must be registered with HRD Corp and have enough levy balance to cover the course fee.
- Request a quotation - contact us for the quotation, course content and trainer profile; you attach these to the grant application.
- Apply for the grant in eTRiS before the class - log in to your employer eTRiS account, go to Application > Grant > Apply Grant, choose the scheme HRD Corp Claimable Courses (SBL-Khas), select us as the training provider and this course, add your trainees, attach the quotation, course content and trainer profile, and submit.
- Submit in time - for public classes, submit at least 3 calendar days before the class (15 June to 31 December 2026) and at least 14 days before from 1 January 2027; in-house classes need 14 days. The training must start within 90 days of that advance submission period.
- Send us the grant approval - once HRD Corp approves the application, email us the grant ID so we can confirm your seats.
- Attend the class - trainees sign the daily attendance (T3) form.
- Claim after the class - we submit the training provider claim in eTRiS with the attendance and invoice; you then complete the employer claim and sign the JD/14 form (manager level or above). Claims must be submitted within 6 months of the training.
More details: our HRD Corp Claimable Courses page and the HRD Corp employer guidelines.
Job Roles
Job Roles
- Financial Analysts
- Investment Analysts
- Finance Professionals
- Accountants
- Data Analysts
Trainers
Trainers
Financial data has been a recurring playground for Dr. Alfred Ang, a full-stack AI developer, Agentic AI solution architect and corporate trainer who serves as CTO of Tertiary Infotech Academy Pte Ltd in Singapore. Dr. Ang builds intelligent agents and business systems end-to-end, from front end and back end to databases, cloud deployment and native mobile apps, and much of that work is written with AI coding assistants and published across more than 300 public GitHub repositories. The n8n-financeadvisor project is a dark-themed stock analysis dashboard with TradingView and TwelveData charts showing live price and volume data, paired with an n8n-powered Telegram AI agent that answers questions about the market. Dr. Ang has also built an investment advisor website with an enquiry and FAQ chatbot workflow, and financial planning sites featuring a retirement calculator and gated planning checklist. On the accounting side, tertiary-hrms is a Next.js HR and accounting platform for Tertiary Infotech that handles leave, attendance, payroll and expense claims, so the realities of messy transactional data are familiar ground. For teaching analysis, data-mining-ml-labs provides interactive Python and Streamlit labs that make data exploration and machine learning approachable for beginners. Across these projects, Dr. Ang routinely uses AI assistants to scaffold code, debug errors and refactor quickly, the same vibe coding workflow this course teaches. That habit of building quickly and verifying carefully is the core discipline Dr. Ang wants every finance learner to adopt.
AI Vibe Coding for Python Financial Analysis shows learners how to get useful financial analysis out of Python without writing every line by hand. The first topic gets everyone started with AI vibe coding, setting up tools such as Cursor, GitHub Copilot and Claude and learning how to describe an analysis clearly enough for the assistant to produce working code. In the second topic, participants analyse financial data with AI help: loading price histories and statements, cleaning gaps, calculating returns and volatility, and checking every result rather than trusting it blindly. The third topic covers portfolio and investment analysis, including diversification, risk and return comparisons and simple scenario testing, using the same kind of logic Dr. Ang built into the stock analysis dashboard. The final topic brings everything together in financial dashboards and reports, turning analysis into charts and summaries that colleagues and managers can actually read. Dr. Ang teaches by building live in front of the class, narrating each prompt, showing the inevitable mistakes and demonstrating how to steer the assistant back on track. Finance professionals, analysts and business owners with little or no coding background will find the pace manageable, while those with some Python experience will pick up faster workflows. Learners who would like to examine the finance dashboards and analysis labs mentioned in class can explore Dr. Participants keep all the notebooks and scripts produced in class, giving them a starter library to adapt for budgeting, forecasting or reporting tasks. Ang's portfolio at https://github.com/alfredang.
Saeid Alizadeh is a data analytics and visualisation specialist with more than eight years of hands-on experience in data processing, visualisation and analytics, and is a Microsoft Certified: Power BI Data Analyst Associate. He has trained Malaysian professionals in data analysis and data visualisation with Tertiary Infotech Sdn. Bhd. since 2016 and has been an HRD Corp Accredited Trainer since 2021, specialising in Power BI, Excel data analysis and Tableau. As Chief Technology Officer of Skymics Sdn. Bhd., a Kuala Lumpur MSC Malaysia-status IoT company, he leads solutions in which data from smart energy monitoring, smart plantation, smart building and environmental monitoring systems is collected, cleaned and turned into dashboards and decisions, and he has also worked on social media analytics and smart-city energy management projects. Earlier in his career he served as IT Director at World Wide Science. He is a Certified Citizen Data Scientist Train-the-Trainer and a certified IBM IoT Practitioner and Instructor, is co-inventor of three inventions, and holds an MBA in Information Technology from Multimedia University and a Bachelor's degree in Computer Science (Information Technology).
On this course, Saeid draws on his data-processing and analytics experience across IoT, energy and business data. He shows learners how Python can clean, analyse and visualise data in a repeatable way, with practical examples drawn from real projects.
Review
Customer Reviews (8)
- Engaging trainer Review by Course Participant/Trainee
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The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 27/09/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Very informative Review by Course Participant/Trainee
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The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 29/04/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment - Great learning experience Review by Course Participant/Trainee
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Excellent delivery and good balance between theory and practice. Learned a lot in a short time. (Posted on 17/01/2025)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
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