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WSQ - Data Mining and Machine Learning Fundamentals for Beginners

WSQ - Data Mining and Machine Learning Fundamentals for Beginners

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.

Learning Outcomes

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.

Course Brochure

Download WSQ - Data Mining and Machine Learning Fundamentals for Beginners Brochure

Skills Framework

This course follows the guideline of Data Analytics MED-ACE-3018-1 TSC under Media Skills Framework

Certification

Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.
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 Funding

WSQ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is subjected to funding caps.

Effective for courses starting from 1 Jan 2024
Full Fee	GST	Nett Fee after Funding (Incl. GST)
		Baseline	MCES / SME
$750.00	$67.50	$442.50	$292.50

Baseline: Singaporean/PR age 21 and above
MCES(Mid-Career Enhanced Subsidy): S'porean age 40 & above

Upon registration, we will advise further on how to tap on the WSQ Training Subsidy.

You can pay the nett fee (after the WSQ training subsidy) by the following :

SkillsFuture Enterprise Credit (SFEC)

Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.Click here to submit SkillsFuture Enterprise Credit

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. Click here for SkillsFuture Credit submission

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. Click here to submit UTAP

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding. Please inform us if you intend to use your PSEA funding.

To check for Post-Secondary Education Account (PSEA) eligibility for this course, Visit SkillsFuture (course code: TGS-2020503264)

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 and email to us.

Course Code: TGS-2020503264

Post-Course Support

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.

Course Cancellation/Reschedule Policy

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commerce.
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

Final Assessment

Practical Performance (PP)
Oral Questioning (OQ)

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 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

Software Requirement

Software:

You can download and install the following software:

Orange

Hardware: Windows and Mac Laptops

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 of age 21 and above
From 1 October 2023, attendance-taking for SkillsFuture Singapore's (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 SkillsFuture Singapore's (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), SSG 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

Steps to Apply Skills Future Claim

The staff will send you an invoice with the fee breakdown.
Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

Note

SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
UTAP support amount will be paid to training provider first and claimed after end of class by learner.

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
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

Dwight Nuwan Fonseka - Dwight Nuwan Fonseka is an ACLP-certified trainer and Head of Data Science at Plano Pte. Ltd., where he leads projects in predictive analytics, big data, and dashboard development.With expertise in R, Python, Tableau, and deep learning frameworks such as Keras and h2oAI, he has developed solutions ranging from healthcare analytics to text mining and time series forecasting. Dwight also serves as an adjunct lecturer at the London School of Business and Finance (LSBF), where he coordinates the Diploma in Data Analytics program, and as an associate trainer with Tertiary Courses, specializing in data mining, machine learning, and visualization.

His training approach emphasizes practical, beginner-friendly learning, guiding participants through key concepts such as classification, regression, and clustering. By incorporating tools like RapidMiner, R, and Orange alongside Python-based frameworks, he ensures learners gain hands-on experience in preparing data, building models, and interpreting results. Dwight’s blend of academic insight and industry practice equips learners with the foundational skills needed to apply data mining and machine learning to real-world problems.

Quah Chee Yong - Quah Chee Yong is an ACLP-certified adult educator with extensive experience in machine learning, NLP, and AI applications. As Data Science Training Lead at MSITEK, he has delivered training programs under SAP, Temasek Polytechnic, and IMDA, covering fundamentals to advanced data science projects. He also served as AI Solutions Lead at AiDeal Scan, where he applied NLP and recommender systems to enhance personalization and customer analytics. His leadership roles include heading the Data Science team at GoWild Singapore, where he built analytics platforms and deployed AI-driven solutions.

Quah specializes in simplifying technical concepts for beginners, ensuring learners gain confidence in applying data mining and machine learning techniques. His training covers supervised and unsupervised learning, data cleaning, and feature engineering using Python, TensorFlow, and scikit-learn. With his track record in developing training curricula and mentoring learners at different levels, Quah ensures participants build a strong foundation to explore and apply machine learning models effectively.

Dr Alvin Ang - Dr Alvin Ang is an ACLP-certified trainer with a Ph.D. 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, Curtin University, and as an IBM Data Science Instructor, covering topics ranging from machine learning to deep learning and big data analytics. As the founder of the open-source data science community DataFrens.sg, he is actively engaged in advancing practical AI and data science skills in Singapore.

Dr Ang has earned multiple IBM certifications in Python, machine learning, and data visualization, which complement his strong teaching and consulting background. His beginner-focused courses in data mining and machine learning emphasize practical coding exercises, model building, and real-world applications. Through his structured and learner-centered approach, he equips participants with the essential skills to explore datasets, apply basic algorithms, and develop a solid foundation in machine learning fundamentals.

Terence Ee - Terence Ee is an independent consultant and trainer with over 25 years of experience in IT management, systems integration, and digital transformation.He has served as Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he successfully led enterprise-scale IT and analytics initiatives. Holding a B.Sc. in Computer Science from NUS and an M.Sc. in Technology Management from Staffordshire University, he combines strong technical expertise with executive-level leadership experience.

Since 2017, Terence has been training professionals in Python, data analytics, and applied machine learning. His beginner-friendly teaching style focuses on step-by-step explanations of data mining processes, covering data preparation, feature selection, and basic supervised/unsupervised learning methods. By blending practical coding with strategic business insights, Terence helps learners build the confidence and skills necessary to apply data mining and machine learning fundamentals in their professional environments.