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WSQ - Data Mining and Machine Learning Fundamentals for Beginners

Sessions 2 days
Duration 16 hrs
Level Beginner
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 Analytics
TSC Code: MED-ACE-3018-1

Funding for this course is valid from 1 Mar 2020 to 28 Feb 2027. Register and complete the course within this period to qualify for funding support.

By the end of the course, learners will be able to

LO1: Apply data mining and machine learning principles to assess business insights.
LO2: Integrate information from datasets.
LO3: Apply predictive data modelling techniques to identify underlying trends in data.
LO4: Apply machine learning classification techniques to gain new insights from data.
LO5: Apply clustering techniques to discover data pattern and make decision.
LO6: Develop prototype algorithms with dimension reduction techniques.
LO7: Construct association rules to Identify patterns across multiple data sets to derive insights.

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 - Data Mining and Machine Learning Fundamentals for Beginners

Course Code: TGS-2020503264

WSQ
SFC
PSEA
UTAP
SFEC
Absentee Payroll
MCES

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Upgrade your data skills with our cutting-edge WSQ Data Mining and Machine This course introduces the fundamentals of Data Mining and Machine Learning, equipping beginners with the necessary skills to apply these principles in assessing business insights and integrating information from datasets for informed decision-making. Participants will gain a solid understanding of the data mining process, the impact of machine learning, and how to leverage these for accessing business insights. Through hands-on learning, the course covers data preparation techniques including import/export, filtering, cleaning, and joining data to ensure quality inputs for analysis.

Delving deeper, the course explores advanced techniques such as predictive data modeling to identify trends, machine learning classification to uncover insights, and clustering techniques for pattern discovery. Participants will also learn about dimension reduction to develop prototype algorithms and construct association rules for pattern identification across multiple datasets. By the end of the course, learners will have the ability to apply these techniques effectively to solve real-world business problems, paving the way for innovative solutions and strategic decisions.

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

Full Fee $750.00 Before GST

GST $67.50 9% of fee

Baseline Nett $442.50 SG/PR age 21+ · 50% funded

MCES / SME Nett $292.50 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

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year.

Submit UTAP Claim

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: Overview of Data Mining and Machine Learning

Data Mining Process

Overview of Machine Learning

Impact of Data Mining and ML to Access Business Insights

Topic 2: Data Preparation

Import/Export Data

Filter Data

Join Data

Clean Data

Topic 3: Regression

What is Regression

Linear Regression

Underfitting and Overfitting

Regularization Techniques

Topic 4: Classification

What is Classification

Classification Algorithms

K-Fold Cross Validation

Model Evaluation Metrics

Confusion Matrix

Topic 5: Clustering

What is Clustering

K-Means Clustering

Silhouette Analysis

Hierarchical Clustering

Topic 6: Dimension Reduction

Principal Component Analysis (PCA)

Feature Ranking

Topic 7: Association Analysis

Association Rules

Constructing Rules

Practical Exam
Oral Questioning

Course Info

Minimum Entry Requirement

Knowledge and Skills

Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
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 18 years old

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

Orange Data Mining – free

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), or partly reimbursed by UTAP for NTUC members.

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-2020503264.
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 UTAP (NTUC Members)

NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.

Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.

Make sure your NTUC membership is paid up before the course starts.
Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
Attend at least 75% of the course and sit all assessments.
Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
NTUC usually pays the claim within 4 weeks of submission.

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
Machine Learning Engineer
Data Analyst
Business Intelligence Analyst
Research Scientist
Quantitative Researcher
Bioinformatics Scientist
Data Mining Specialist
Customer Insights Analyst
Marketing Analytics Specialist
Predictive Analytics Specialist
Healthcare Data Analyst
Financial Modeler
E-commerce Data Specialist
User Behavior Analyst

Trainers

Dr. Alfred Ang has published hands-on labs for this very course: his GitHub repository data-mining-ml-labs offers interactive Python and Streamlit labs for beginner data mining and machine learning, built for WSQ course TGS-2020503264. That blend of courseware and code reflects his roles as a full-stack AI developer, Agentic AI solution architect, courseware developer and corporate trainer, and as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd. His machine learning credentials are substantial. He is a Microsoft Certified Data Scientist (DP-100), Microsoft Certified AI Engineer Associate (AI-102) and Power BI Data Analyst (PL-300), an AWS Certified AI Practitioner, a Neo4j Certified Professional, and an IBM certified instructor for the AI Practitioners course. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU, a B.Sc (Hons) in Physics from NUS and a Graduate Diploma in Intelligent Robotics Systems from NUS-ISS, and has published more than 20 peer-reviewed papers and co-invented more than 20 US patents after working in the defence, electronics and semiconductor industries. As an ACTA-certified trainer and DACE-certified curriculum developer, he designs Skills Framework-aligned WSQ programmes that turn complex techniques into practical learning for working professionals. He also brings an entrepreneur's eye for business value, having founded Tertiary Infotech in 2012 and grown it into Tertiary Courses, so his examples connect models to the decisions organisations make about customers, operations and sales.

In WSQ - Data Mining and Machine Learning Fundamentals for Beginners, Dr. Ang takes learners step by step from business questions to working models. The course opens with the data mining process and the impact of machine learning on business insights, then moves to data preparation: importing and exporting, filtering, joining and cleaning data, which he treats as the foundation of every reliable model. Learners build regression models, exploring linear regression, underfitting, overfitting and regularisation, before turning to classification with common algorithms, k-fold cross-validation, evaluation metrics and the confusion matrix. Clustering follows with k-means, silhouette analysis and hierarchical clustering, then dimension reduction with principal component analysis and feature ranking, and finally association analysis and rule construction. Because the labs are his own, he can explain exactly why each exercise is set up the way it is and adapt it to the questions learners bring. His style is patient and practical, suited to beginners: he explains the intuition behind each algorithm before the code, encourages learners to interpret results in business terms and keeps the focus on informed decision-making rather than mathematics for its own sake. As a WSQ course, it prepares learners for assessment while giving them skills they can use at work. Learners also see how the same techniques underpin many of today's AI tools. To try related labs and see his wider AI projects, learners can view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, data mining and machine learning. He has extensive hands-on experience developing predictive models, deep learning solutions and RShiny dashboards using R, Python, TensorFlow and Keras, and his professional background spans healthcare, finance and customer analytics. Dwight brings an applied perspective to teaching AI that covers both the opportunities and the risks of emerging technologies. He also holds a Masters in Education from NTU and spent eight years teaching in international schools in Singapore and overseas.

In WSQ Data Mining and Machine Learning Fundamentals for Beginners, Dwight draws on his everyday modelling work to make core concepts accessible. He walks learners through the data mining process and how machine learning yields business insights, then through data preparation, including importing, filtering, joining and cleaning data. Participants go on to regression, covering underfitting, overfitting and regularisation, and classification, with cross-validation, evaluation metrics and confusion matrices, before exploring clustering. His teaching background helps beginners build confidence step by step. Learners leave able to prepare datasets, build and evaluate simple models, and use the results to support informed business decisions.

Quah Chee Yong is an ACLP-certified adult educator with extensive experience in machine learning, natural language processing and AI applications. As Data Science Training Lead at MSITEK, he has delivered programmes for SAP, Temasek Polytechnic and IMDA, from fundamentals to advanced data science projects. He served as AI Solutions Lead at AiDeal Scan, applying NLP and recommender systems to personalisation and customer analytics, and headed the data science team at GoWild Singapore, where he built analytics platforms and deployed AI-driven solutions.

WSQ Data Mining and Machine Learning Fundamentals for Beginners suits his strength in simplifying technical concepts for newcomers. Learners start with the data mining process and an overview of machine learning and its impact on business insights, then prepare data by importing, exporting, filtering, joining and cleaning it. They go on to regression, including underfitting, overfitting and regularisation, and classification, including cross validation, evaluation metrics and the confusion matrix, before moving to clustering. Drawing on his experience in data cleaning, feature engineering and curriculum development, he helps participants build a solid foundation for applying machine learning to business decisions.

Dr Alvin Ang is an ACLP-certified trainer with a PhD in Operations Research from Nanyang Technological University and more than a decade of experience in data science and AI education. He has taught at NTU, SUSS and Curtin University, and as an IBM Data Science Instructor, covering topics from machine learning to deep learning and big data analytics. He holds multiple IBM certifications in Python, machine learning and data visualisation, which complement his teaching and consulting background, and he founded the open-source data science community DataFrens.sg to advance practical AI and data science skills in Singapore.

WSQ Data Mining and Machine Learning Fundamentals for Beginners reflects his beginner-focused teaching, which emphasises practical coding exercises, model building and real-world applications. Learners begin with the data mining process and how machine learning produces business insights, then practise preparing data by importing, filtering, joining and cleaning it. They go on to regression, including overfitting and regularisation, classification with cross-validation and evaluation metrics, clustering, principal component analysis and association rules. Dr Ang's structured, learner-centred approach makes these ideas approachable for newcomers, so participants leave able to explore datasets, apply core algorithms and build a solid foundation in machine learning.

Terence Ee is an independent consultant and trainer who, since 2017, has trained professionals in Python, data analytics and applied machine learning. He brings over 25 years of experience in IT management, systems integration and digital transformation, including senior roles as Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he led enterprise-scale IT and analytics initiatives. He holds a B.Sc. in Computer Science from NUS and an M.Sc. in Technology Management from Staffordshire University, and is an ACLP-certified, WSQ-accredited trainer.

His beginner-friendly teaching style suits this course well. Terence explains the data mining process step by step, starting with how data mining and machine learning generate business insight, then moving through data preparation, including importing, filtering, joining and cleaning data. Learners then build and evaluate regression and classification models, learning to recognise underfitting and overfitting, apply regularisation, use k-fold cross validation and read a confusion matrix. Later topics cover clustering with k-means and hierarchical methods, dimension reduction with PCA and feature ranking, and association analysis. By blending practical coding with strategic business insight, he helps participants gain the confidence to apply these fundamentals in their own work.