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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.

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 Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.

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

Promotion Code

Promo or discount cannot be applied to WSQ courses

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

Softtware: Windows / Mac

Hardware: Laptop

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity.

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Individual Sponsored Trainee

Employer Sponsored Trainee

Singapore Citizens or Singapore Permanent Residents aged 21 and above
From 1 October 2023, attendance-taking for SWDA's (formerly SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria.
Singapore Citizens or Singapore Permanent Residents who are DIRECT EMPLOYEE of the sponsoring company.
From 1 October 2023, attendance-taking for SWDA's (formerly SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria.

SkillsFuture Credit:

Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.

PSEA:

To check for Post-Secondary Education Account (PSEA) eligibility, goto mySkillsFuture portal and search for this course code.
Scroll down to "Keyword Tags" to verify for PSEA eligibility.
If there is “PSEA” under keyword tags, the course is eligible for PSEA.
And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA.
Not all courses are eligible for PSEA funding.

Absentee Payroll (AP) Funding:

$4.50 per hour, capped at $100,000 per enterprise per calendar year.
AP funding will be computed based on the actual number of training hours attended by the trainee.

SFEC:

If the Training Provider has submitted an enrolment for course fee grant claim in Training Partners Gateway (TPGateway), SWDA would be able to derive SFEC funding based on this record. There is no need for enterprise to submit any claim request and the SFEC claim will be automatically generated and disbursed.
Where there is no such record, eligible employers are required to submit an SFEC claim after course completion via the SFEC microsite.
SkillsFuture Enterprise Credit (SFEC) Microsite

Appeal Process

The candidate has the right to disagree with the assessment decision made by the assessor.
When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
The candidate must lodge the appeal within 7 days, giving reasons for appeal
The assessor can help the candidate with writing and lodging the appeal.
he assessment manager will collect information from the candidate & assessor and give a final decision.
A record of the appeal and any subsequent actions and findings will be made.
An Assessment Appeal Panel will be formed to review and give a decision.
The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
Please click the link below to fill up the Candidates Appeal Form.

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

Yeo Hwee Theng is a data science leader and AI strategist with extensive experience in driving enterprise analytics, AI adoption, and data architecture initiatives across healthcare, finance, and government sectors. As the Data & Analytics Product Lead at Amplify Health, she leads large-scale data transformation and machine learning projects focused on improving business outcomes through data-driven decision-making. Her previous roles include AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot, where she implemented advanced AI and analytics solutions across the Asia-Pacific region. 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).

Tan Woei Ming is an accomplished data scientist and AI engineer with over 15 years of experience in artificial intelligence, machine learning, and data-driven innovation. Holding a Master’s in Intelligent Systems from the National University of Singapore (NUS) and a First-Class Honours in Electrical and Electronic Engineering from NTU, he has led AI initiatives in predictive analytics, automation, and process optimization across the semiconductor and manufacturing industries. His expertise lies in translating complex AI technologies into practical business applications, enabling organizations to innovate through data insights and intelligent automation.

Teh Siew Yee is an experienced adult educator and corporate trainer specializing in business communication, workplace effectiveness, and professional development. With years of experience across both corporate and training environments, she has helped learners enhance their skills in problem-solving, collaboration, and interpersonal communication. Her ability to translate complex concepts into clear, practical strategies ensures that participants can immediately apply their learning in the workplace. As an ACLP-certified trainer, Siew Yee delivers WSQ courses with a strong focus on learner engagement and workplace application. She integrates case studies, role-plays, and reflective exercises into her sessions, ensuring participants develop not only knowledge but also confidence in real-world contexts. By combining her corporate experience with adult education expertise, she empowers learners to improve workplace efficiency, strengthen teamwork, and achieve personal and organizational success.

Truman Ng is a cloud computing and AI infrastructure specialist with over 20 years of experience in enterprise networking, cybersecurity, and automation. A PMP, ACTA, and Huawei HCIE certified professional, he has trained corporate teams globally in DevOps, AI systems integration, and cloud deployment. His expertise lies in bridging infrastructure and AI engineering, helping organizations build scalable, secure, and data-driven systems for modern enterprises.
In “AI Vibe Coding for Data Mining and Modeling,” Truman teaches how to build AI-assisted data pipelines and computational models in hybrid and cloud environments. His sessions emphasize secure architecture, model orchestration, and performance optimization. By merging practical engineering with AI reasoning concepts, he helps learners design robust, production-ready data mining and modelling solutions that support enterprise-level data intelligence.

James Lee is a veteran digital media and IT educator with over two decades of experience in creative technology, automation, and digital transformation. An Adobe Certified Expert and ACLP-qualified instructor, he has trained professionals in digital communication, AI-powered productivity, and information design across universities and corporate programs. His teaching philosophy centers on making advanced technology concepts accessible through visual and experiential learning.
In “AI Vibe Coding for Data Mining and Modeling,” James focuses on the creative and practical aspects of visualizing mined data and integrating LLMs into data storytelling. His sessions guide learners to apply Python visualization tools, prompt engineering, and generative models for building intelligent dashboards and narrative-driven insights. With his strong design and technology background, he enables participants to communicate complex relationships and insights effectively using AI vibe coding.

Review

Customer Reviews (5)

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

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?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

. (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)