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WSQ - AI Vibe Coding for Data Mining and Modeling

Sessions 2 days
Duration 16 hrs
Level Intermediate
Assessment 2 hrs

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

TSC Title: Data Mining and Modelling
TSC Code: STP-DAT-3003-1.1

Funding for this course is valid from 16 May 2024 to 15 May 2027. Register and complete the course within this period to qualify for funding support.

By end of the course, learners should be able to:

LO1: Develop data science guidelines to enhance data-mining applications.
LO2: Identify and rectify data problems.
LO3: Construct graph machine learning models to identify patterns and trends in data sets.
LO4: Perform narrative analytics using Large Language Model (LLM) models.

Download Course Brochure

Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.
OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ - AI Vibe Coding for Data Mining and Modeling

Course Code: TGS-2024045802

WSQ
SFC
PSEA
SFEC
Absentee Payroll
MCES

5 Review(s) | Add Your Review

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, clean, transform, and explore structured and unstructured data. They will apply data mining methods such as classification, regression, clustering, association analysis, anomaly detection, similarity analysis, and pattern discovery to address real-world business and technical problems. The course also covers feature engineering, data visualisation, model selection, and the interpretation of complex relationships within datasets.

Building on these foundations, learners will design and evaluate computational models using suitable algorithms and performance metrics. They will use AI-assisted coding to compare modelling approaches, optimise parameters, identify issues such as overfitting and data leakage, and improve model reliability. Where appropriate, learners will also explore graph-based modelling to analyse relationships, communities, connections, and paths within interconnected data.

Through hands-on projects, participants will develop end-to-end Python workflows that transform raw data into validated models, visual findings, and actionable recommendations. By the end of the course, learners will be able to use AI vibe coding to select appropriate algorithms, build reliable data mining models, evaluate results, and communicate insights for evidence-based decision-making.

This course is part of our AI training in Singapore, where you can browse all our AI courses. See more AI vibe coding courses.

Full Fee $900.00 Before GST

GST $81.00 9% of fee

Baseline Nett $531.00 SG/PR age 21+ · 50% funded

MCES / SME Nett $351.00 SG age 40+ · 70% funded

SkillsFuture Enterprise Credit (SFEC)

Eligible companies can use their $10,000 SFEC to cover up to 90% of out-of-pocket course fees for classes ending on or before 30 Nov 2026. A redesigned SFEC starts on 1 Dec 2026.

View on SkillsFuture for Business

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used.

Direct Application on SkillsFuture Portal

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding.

Check PSEA Eligibility

Scroll down to “Keyword Tags” to verify for PSEA eligibility.
If there is “PSEA” under keyword tags, the course is eligible for PSEA.

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form, then submit the completed form to us:

PSEA Withdrawal Form Submit PSEA Form

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

We provide free consultation related to the subject matter after the course.
Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Topic 1: Data Science Guidelines and Data Preparation

Topic 2: Graph Data Mining, Pattern Discovery and Algorithm Selection

Topic 3: Graph Machine Learning for Classification, Clustering and Prediction

Topic 4: LLM-Powered Narrative Analytics and Insight Generation

Written Exam
Practical Exam

Course Info

Minimum Entry Requirement

Knowledge and Skills

Able to operate using computer functions
Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

Positive Learning Attitude
Enthusiastic Learner

Experience

Minimum of 1 year of working experience.

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

Funding Eligibility Criteria

Self-sponsored learners (individuals)

Singapore Citizens and Singapore Permanent Residents aged 21 and above.
Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.

Company-sponsored learners

Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
Employers can also claim Absentee Payroll funding for employees who attend during working hours.
Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).

Conditions for all funded learners

Attendance is taken digitally with Singpass for every session, including the assessment.
You must complete the course and pass all assessments (assessed as Competent).
If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
9% GST is charged on the full course fee before funding.
Promo and discount codes cannot be used on funded courses.
Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA).

How to Apply SkillsFuture Credit Claim for Individuals

Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.

Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
We email you an invoice showing the course fee after funding.
From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
Click Submit a Claim and search for this course by its course code, TGS-2024045802.
Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.

Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.

How to Apply SkillsFuture Enterprise Credit (SFEC)

SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.

Current SFEC: classes ending on or before 30 November 2026

Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.

Check your company's SFEC eligibility and balance on EnterpriseSG.
Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.

Any unused balance of the current SFEC expires on 30 November 2026.

Redesigned SFEC from 1 December 2026

Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.

How to Apply PSEA

Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.

Check that this course shows the PSEA tag on its MySkillsFuture page.
Check your PSEA balance on MOE's Student Finance System with Singpass.
Download and complete the PSEA withdrawal form, then submit it to us.
We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.

More details: MOE PSEA overview.

Appeal Process

If you disagree with an assessment decision, you have the right to appeal.

When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
Discuss the decision with the assessor or the assessment manager first.
If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.

Job Roles

Data Scientist
Data Analyst
Data Mining Specialist
Machine Learning Engineer
Business Intelligence Analyst
Data Engineer
Predictive Analytics Specialist
Data Analytics Consultant
Research Analyst
Operations Analyst
Data Modelling Specialist
AI Solutions Architect
Data Visualization Expert
Statistical Analyst
Data Strategy Consultant
Product Manager (focused on data products)
Innovation Specialist
Business Owner or Manager working with data

Trainers

Dr. Alfred Ang has spent his career turning data into models, first as a researcher and now as a full-stack AI developer. He holds a PhD in Electrical Engineering from the National University of Singapore, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, and his earlier work in the defence, electronics and semiconductor industries produced more than 20 peer-reviewed papers and more than 20 US patents as co-inventor. Today he is an Agentic AI solution architect and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, having founded Tertiary Courses. His credentials fit this course closely: he is a Neo4j Certified Professional, which grounds his teaching of graph data, as well as a Microsoft Certified Data Scientist (DP-100), AI Engineer Associate (AI-102) and Power BI Data Analyst (PL-300), and an AWS Certified AI Practitioner. He is also an IBM certified instructor for the AI Practitioners course. His GitHub portfolio of more than 300 public repositories includes data-mining-ml-labs, a set of interactive Python and Streamlit labs for beginner data mining and machine learning, and n8n-inventorycontrol, an AI audit and forecasting agent in which a local LLM reads stock levels, flags items below their reorder threshold, computes replenishment and reports the findings. He builds much of this work through AI-assisted coding, directing agents in plain English and reviewing the results.

In WSQ AI Vibe Coding for Data Mining and Modeling, Dr. Ang teaches learners to use AI coding assistants and Python to mine data and build models without writing every line by hand. The course opens with data science guidelines and data preparation, then turns to graph data mining, pattern discovery and choosing the right algorithm for the question. From there learners apply graph machine learning to classification, clustering and prediction, and finish with LLM-powered narrative analytics that turn results into clear, decision-ready insight. His Neo4j certification and data-science background shape how he explains graphs, relationships and the features that can be extracted from them, while his daily use of AI coding agents shapes the workflow: describe the analysis in natural language, have the assistant generate and explain the code, then test, debug and refine it until the output can be trusted. He insists that learners check data quality, question surprising results and understand what each model is doing, so that vibe coding speeds up the work without hiding the reasoning. Learners build and run their own notebooks and applications through the course, and he provides prompts and patterns they can reuse on their own datasets. As an ACTA-certified trainer and DACE-certified curriculum developer, he delivers sessions aligned to the WSQ competency standards with plenty of guided practice. Practical examples, from customer networks to product relationships, show where relationship-based analysis reveals patterns that ordinary tables can miss. To see his data and machine learning projects, explore his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Yeo Hwee Theng is a data science leader and AI strategist with extensive experience in enterprise analytics, machine learning and data architecture across healthcare, fintech and government. As Data & Analytics Product Lead at Amplify Health, she oversees AI-driven transformation initiatives and designs advanced data science solutions and enterprise analytics architectures. Her earlier roles as AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot saw her deliver large-scale AI and analytics deployments across Asia. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).

In AI Vibe Coding for Data Mining and Modeling, she combines data architecture, applied analytics and AI strategy to help professionals mine data and build computational models with AI coding assistants. Learners begin with data science guidelines and data preparation, move into graph data mining, pattern discovery and algorithm selection, and then apply graph machine learning to classification, clustering and prediction. The final topic uses large language models to turn results into clear narrative insights. Throughout, she shows learners how to direct an assistant to prepare data, choose algorithms and interpret relationships in complex datasets, so they leave able to build reliable data mining workflows without hand-writing every line of code.

Tan Woei Ming is a data scientist and AI engineer with more than 15 years of experience in machine learning, deep learning and data automation for the semiconductor industry. At Micron Semiconductor Asia, he led projects in predictive analytics, image recognition and robotic defect detection, deploying TensorFlow, PyTorch and Spark frameworks for large-scale analytics and yield optimisation. He holds a Master's in Intelligent Systems from NUS and a First-Class Honours degree in Electrical and Electronic Engineering from NTU. His work bridges data science with industrial AI applications, translating complex techniques into practical business value.

In WSQ - AI Vibe Coding for Data Mining and Modeling, Woei Ming guides learners in building computational models for enterprise and industrial use cases with the help of AI-assisted coding. Participants apply data science guidelines and data preparation, explore graph data mining, pattern discovery and algorithm selection, use graph machine learning for classification, clustering and prediction, and generate narrative insights with large language models. Drawing on his experience in data engineering and model deployment, he helps learners build end-to-end Python pipelines and evaluate the models that AI tools help them produce, so their analytics are both faster to build and sound.

Teh Siew Yee is a data analytics and digital transformation leader with more than two decades of experience across the technology, banking and manufacturing sectors. Siew Yee has held leadership roles at Standard Chartered, Hewlett-Packard, TikTok and SIA Engineering, leading teams in AI governance, data management and business analytics. Siew Yee holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and a Bachelor of Engineering (Electrical and Electronic) from Nanyang Technological University. A certified ACLP trainer, Siew Yee is known for engaging, application-driven courses in data science and business intelligence.

In this course, Siew Yee combines that analytics leadership with practical Python work to show professionals how AI coding assistants can speed up data mining and modelling. Learners start with data science guidelines and data preparation, then move into graph data mining, pattern discovery and choosing suitable algorithms. They go on to apply graph machine learning for classification, clustering and prediction, and finish by using large language models to turn model results into clear narrative analytics and insights. Throughout, learners use natural-language instructions to generate, explain, test and refine code, and Siew Yee's blend of technical rigour and business perspective helps them apply these techniques to real transformation initiatives.

Truman Ng specialises in bridging infrastructure and AI engineering, helping organisations build scalable, secure and data-driven systems. A cloud computing and AI infrastructure specialist with over 20 years of experience in enterprise networking, cybersecurity and automation, he has trained corporate teams around the world in DevOps, AI systems integration and cloud deployment. He holds PMP and Huawei HCIE certifications and is an ACTA-certified trainer, known for merging practical engineering with AI reasoning concepts in a hands-on, applied way.

In WSQ AI Vibe Coding for Data Mining and Modeling, Truman teaches participants to build AI-assisted data pipelines and computational models using Python and natural-language prompts. Learners follow data science guidelines to collect, clean and prepare data, explore graph data mining and pattern discovery, and select suitable algorithms, before applying graph machine learning to classification, clustering and prediction. The course closes with LLM-powered narrative analytics that turn results into clear insights. Throughout, his sessions emphasise secure architecture, model orchestration and performance optimisation, helping learners design robust, production-ready data mining and modelling solutions for enterprise data.

Review

Customer Reviews (5)

will recommend Review by Course Participant/Trainee

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2. Do you find the trainer knowledgeable in this subject?
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Some parts of the slides shared were outdated. Perhaps could update them so for future participants (Posted on 5/9/2025)

will recommend Review by Course Participant/Trainee

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. (Posted on 10/29/2024)

will recommend Review by Course Participant/Trainee

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

. (Posted on 10/29/2024)

Will Recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
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. (Posted on 3/31/2019)

Will Recommend Review by Course Participant/Trainee

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

Provide detailed training notes including the steps , in addition to training notes , together with sample codes as tutorials

nstead of one full Sunday, which is difficult to absorb, split to 4 afternoons/4 mornings on weekends .Better chance for student to absorb and practice. (Posted on 1/13/2019)