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WSQ - Data Analytics and AI for Healthcare

WSQ - Data Analytics and AI for Healthcare

Dive into the transformative world of data analytics and AI in healthcare with our comprehensive course. You will learn how to harness the power of AI insights to significantly improve healthcare outcomes and make informed decisions that benefit both patients and organizations. Our expert instructors will guide you in managing and prioritizing healthcare data projects, ensuring they align with your organizational goals and deliver maximum benefits.

In this course, you will also develop the skills to extract valuable insights from vast amounts of healthcare data, and use these insights to inform and shape effective strategies. Furthermore, you will learn how to communicate results and guide decision-making processes using advanced AI and data science methods. By the end of this course, you will be well-equipped with the knowledge and skills to make a significant impact in the healthcare industry, driving improvements and innovations through data analytics and artificial intelligence.

Learning Outcomes

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

LO1: Apply AI insights to improve healthcare outcomes and decision-making.
LO2: Manage and prioritize healthcare data projects for maximum organizational benefit.
LO3: Extract and apply valuable insights from healthcare data to inform strategies.
LO4: Communicate results and guide decision-making using advanced AI and data science methods.

Course Brochure

Download WSQ - Data Analytics and AI for Healthcare Brochure

Skills Framework

This course follows the guideline of Data Analytics HCE-BIN-4104-1.1 under Healthcare 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
$800	$72.00	$472.00	$312.00

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

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form and email to us.

Course Code: TGS-2023041022

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

Topic1 Introduction to AI in Healthcare

Understanding the Benefits of AI in Healthcare
Interpreting Data Patterns in Healthcare
Extracting Insights from Healthcare Data
Case Studies:

AI Applications in Healthcare
Using AI for Early Diagnosis of Diseases
AI-Powered Predictive Analytics for Patient Outcomes

Topic 2 Data Science and Business Insights

Evaluating Data Science Solutions for Healthcare
Managing Data Science Projects in Healthcare
Prioritizing Data Science Projects for Maximum ROI
Customizing Data Models for Healthcare Hypotheses
Case Studies:

Implementing AI for Patient Monitoring and Care
Data Science in Drug Discovery and Development

Topic 3 Data Mining and Analysis

Running Complex Data Mining Models in Healthcare
Managing Organizational Capacity for Data Science Projects
Exploring Healthcare Data Sets Visually and Analytically
Case Studies:

Successful Data Mining Applications in Healthcare
AI for Predicting Disease Outbreaks
Data Mining for Personalized Treatment Plans

Topic 4 Advanced Data Science Techniques

Communicating the Results of Data Science Projects
Making Recommendations Based on Data Insights
Application of Statistics and Data Mining in Healthcare
Tools and Techniques for Advanced Data Modeling
Measuring the Capability of the Data Science Team
Case Studies:

AI in Medical Imaging for Accurate Diagnostics
Machine Learning Models for Predicting Patient Readmission Rates

Final Assessment

Written Assessment - Short Answer Questions (WA-SAQ)
Practical Performance (PP)

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.

Target Age Group: 21 to 65 years old

Minimum Software/Hardware Requirement

Software:

Download and Install the following software

Hardware: Window or 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
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
Healthcare Analyst
Clinical Researcher
Medical Practitioner
AI Developer
Machine Learning Engineer
Health Informatics Specialist
Biostatistician
Medical Imaging Specialist
Robotics Engineer
Bioinformatician
Pharmacologist
Genetic Counselor
Public Health Specialist
Medical Writer

Trainers

Tan Woei Ming: Tan Woei Ming is a data scientist and AI engineer with more than 15 years of experience in predictive analytics, machine learning, and data automation. He holds a Master’s in Intelligent Systems from the National University of Singapore and a First-Class Honours in Electrical and Electronic Engineering from Nanyang Technological University. His work spans industrial automation, medical imaging, and real-time data processing systems, where he has applied AI to improve operational efficiency and decision support. Woei Ming is known for his ability to bridge complex data science techniques with practical, domain-specific applications.

In “Data Analytics and AI for Healthcare,” Woei Ming teaches participants how to apply AI and data-driven methodologies to improve clinical outcomes and healthcare operations. His sessions focus on data preprocessing, model development, and diagnostic prediction using Python and machine learning frameworks. By combining technical rigor with practical case studies, he empowers learners to leverage analytics and AI tools to drive evidence-based decision-making in the healthcare sector.

Yeo Hwee Theng: Yeo Hwee Theng is a data science and analytics leader with extensive experience leading enterprise AI projects across healthcare, finance, and government sectors. As the Data & Analytics Product Lead at Amplify Health, she has driven AI adoption in healthcare analytics, improving patient outcomes through predictive modeling and data integration. Previously, she served as AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and is ACLP-certified.

In “Data Analytics and AI for Healthcare,” Hwee Theng guides learners through healthcare-specific AI applications, including patient risk modeling, resource optimization, and clinical decision support. Her sessions emphasize ethical AI use, data governance, and model deployment in healthcare systems. With her real-world experience in digital health transformation, she equips participants to design and implement scalable AI solutions that enhance care quality and operational efficiency.

Teh Siew Yee: Teh Siew Yee is a digital transformation and data analytics expert with over 20 years of experience in the technology, banking, and healthcare industries. He has led data governance and analytics initiatives at major organizations such as Hewlett-Packard, Standard Chartered, TikTok, and SIA Engineering. Siew Yee holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and a Bachelor of Engineering from NTU. As an ACLP-certified trainer, he specializes in integrating AI and data strategies into practical business solutions.

In “Data Analytics and AI for Healthcare,” Siew Yee focuses on applying data analytics for healthcare management, operational efficiency, and patient care enhancement. His sessions explore the use of AI models for predictive diagnosis, anomaly detection, and workflow optimization. By combining healthcare analytics frameworks with hands-on tools, he helps learners develop a strong foundation in data-driven decision-making and digital innovation within the healthcare context.

Truman Ng: Truman Ng is a senior IT consultant and AI systems architect with more than two decades of experience in cloud infrastructure, automation, and intelligent system integration. A PMP, ACTA, and Huawei HCIE-certified professional, he has delivered global training programs and enterprise solutions in AI deployment, cloud computing, and data analytics. His expertise lies in developing secure, scalable systems for data-driven organizations, bridging the gap between IT infrastructure and advanced analytics.

In “Data Analytics and AI for Healthcare,” Truman focuses on the technical aspects of deploying AI solutions within healthcare environments. His sessions cover data engineering, cloud architecture for health data systems, and AI model deployment with compliance to data security standards. By combining infrastructure design with analytical strategy, he enables learners to implement end-to-end healthcare AI solutions that are reliable, secure, and compliant with regulatory frameworks.

James Lee Kin Nam: James Lee is a digital media and technology educator with over 20 years of experience in multimedia, creative automation, and applied computing. An Adobe Certified Expert and ACLP-qualified instructor, he has trained professionals in digital transformation, AI-driven creativity, and productivity technologies. His teaching style focuses on making emerging technologies accessible and actionable for professionals across diverse industries.

In “Data Analytics and AI for Healthcare,” James introduces participants to the visualization and communication aspects of healthcare analytics. His sessions focus on using data storytelling, dashboard design, and visualization tools to present complex medical data effectively. By blending design principles with analytical insight, he equips learners to create clear, impactful data visualizations that support better healthcare decision-making and communication

Customer Reviews (13)

Recommended 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 11/11/2025)

Recommended 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

Nice course (Posted on 11/11/2025)

Recommended 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 11/11/2025)

Trainer is very clear and engaging 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

Trainer is very clear and engaging. However, too many breaks! Shorter breaks to maximise learning (Posted on 11/11/2025)

Recommended 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 11/11/2025)