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WSQ - AI for Life Science and Bioinformatics

Sessions 4 days
Duration 32 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 and Statistical Analytics
TSC Code: HCE-DAT-4007-1.1

Funding for this course is valid from 19 Oct 2024 to 18 Oct 2026. Register and complete the course within this period to qualify for funding support.

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

LO1: Recognize significant trends and aberrant results in bioinformatics data using statistical techniques.
LO2: Use statistical tests and data analytics tools to estimate uncertainties and determine data acceptability.
LO3: Review datasets to uncover trends or patterns and identify potential causes of unacceptable data.
LO4: Develop new methods for analyzing large, complex bioinformatics datasets using specialized modeling software.
LO5: Facilitate discussions on applying big data analytics to examine bioinformatics issues and 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 - AI for Life Science and Bioinformatics

Course Code: TGS-2024049780

WSQ
SFC
PSEA
SFEC
Absentee Payroll
MCES

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This course equips participants with practical skills to apply artificial intelligence and computational methods to life science and bioinformatics data. Learners will explore how biological information can be collected, prepared, analysed, and interpreted to support research in genomics, transcriptomics, proteomics, drug discovery, and personalised healthcare.

Participants will learn the foundations of bioinformatics, including biological databases, sequence analysis, alignment, gene expression, genomic variation, and protein structure analysis. They will work with complex biological datasets to identify patterns, compare sequences, detect variants, and investigate relationships between genes, proteins, biological functions, and disease-related processes.

The course also introduces machine learning techniques for classifying biological data, predicting outcomes, detecting anomalies, clustering samples, and identifying important features. Learners will apply suitable evaluation methods to assess model performance and avoid issues such as overfitting, biased data, and incorrect biological interpretations.

Through hands-on activities, participants will use AI-assisted analytical workflows to explore datasets, visualise results, summarise scientific findings, and communicate evidence-based insights. Emphasis is placed on data quality, reproducibility, privacy, ethical use, model transparency, and the validation of AI-generated conclusions by qualified professionals.

By the end of the course, learners will be able to develop bioinformatics analysis workflows, apply AI techniques to life science data, evaluate analytical results, and present meaningful findings that support biological research and informed scientific decision-making.

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

Full Fee $2,000.00 Before GST

GST $180.00 9% of fee

Baseline Nett $1,180.00 SG/PR age 21+ · 50% funded

MCES / SME Nett $780.00 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: Bioinformatics Fundamentals and Biological Sequence Analysis

Topic 2: Protein Structure Analysis and Genomic Variant Detection

Topic 3: Transcriptomics, Genomics and Gene Expression Analysis

Topic 4: Machine Learning for Biological Pattern Discovery and Prediction

Topic 5: Bioinformatics Data Visualisation and Scientific Insight Communication

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.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

You will need the following for this course:

A free account on a generative AI assistant of your choice, e.g. ChatGPT, Google Gemini or Microsoft Copilot (the free tier is sufficient)

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-2024049780.
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

Bioinformatics Analyst
Data Scientist (Bioinformatics)
Computational Biologist
Biostatistician
Genomics Data Scientist
Bioinformatics Research Scientist
Molecular Data Analyst
Proteomics Data Analyst
Genomic Data Analyst
Clinical Bioinformatics Specialist
Bioinformatics Software Developer
Machine Learning Engineer (Life Sciences)
Bioinformatics Consultant
Biotechnologist
Biomedical Data Scientist
AI Engineer (Life Sciences)
Big Data Analyst (Life Sciences)
Research Associate (Genomics)
Systems Biologist
Healthcare Data Scientist

Trainers

Dr. Alfred Ang brings a scientist's training and a data scientist's toolkit to the application of AI in the life sciences. He holds a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, where he topped his Physics cohort, and he has published more than 20 peer-reviewed papers and co-invented more than 20 US patents, so the discipline of working from data to evidence to publication is familiar ground. He is a Microsoft Certified Data Scientist (DP-100), AI Engineer Associate (AI-102) and Power BI Data Analyst (PL-300), a Neo4j Certified Professional, an AWS Certified Solutions Architect and AI Practitioner, and an IBM certified instructor for the AI Practitioners course. As a full-stack AI developer and Agentic AI solution architect, his experience spans data engineering and machine learning, and his interests include machine learning, deep learning and artificial intelligence. He is the founder of Tertiary Courses and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, where he designs and delivers Skills Framework-aligned WSQ programmes. His public GitHub portfolio includes data-mining-ml-labs, a set of interactive Python and Streamlit labs that take beginners through data mining and machine learning step by step, and quantumlabs, an interactive set of visual lessons and simulators that shows his approach to explaining complex scientific ideas. His strength in this course lies in the computational and machine learning methods, which he connects to the biological questions learners bring from their own research.

In this WSQ AI for Life Science and Bioinformatics course, Dr. Ang focuses on the computational skills that let researchers move from raw biological data to defensible insight. The first topic covers bioinformatics fundamentals and biological sequence analysis, including biological databases, sequence retrieval, alignment and comparison. The second topic turns to protein structure analysis and genomic variant detection, where learners examine structures and identify variants within datasets. In the third topic learners work with transcriptomics, genomics and gene expression data, preparing and normalising datasets and investigating relationships between genes and conditions. The fourth topic, machine learning for biological pattern discovery and prediction, is where his data science certification and teaching experience come to the fore: he shows how to choose features, train and validate models, avoid data leakage and overfitting, and interpret what a model has and has not learned, the same careful method he teaches in his machine learning labs. His graph database certification also helps him explain how networks of relationships, such as genes, proteins and pathways, can be represented and queried. The final topic covers bioinformatics data visualisation and scientific insight communication, drawing on his publication record to help learners present results clearly and honestly to colleagues and decision-makers. He works in Python with hands-on notebooks throughout, so learners build reusable analysis pipelines rather than one-off results, and he keeps the coding accessible for life scientists who are newer to programming. As an ACTA-certified trainer and DACE-certified curriculum developer, he aligns each activity with the WSQ assessment. To see his data and machine learning projects, 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 combines a life science foundation with professional data science expertise. He holds a degree in Biotechnology from NUS and an Advanced Diploma in Pharmaceutical Management from MDIS, and is Head of Data Science at Plano Pte. Ltd., where his expertise covers data analytics, machine learning and AI applications. He has developed predictive models and deep learning solutions using R, Python, TensorFlow and Keras, with a professional background that includes healthcare. An ACLP-certified trainer with a Masters in Education from NTU, he also spent eight years teaching biology at O Level, A Level and IB level in Singapore and overseas.

This mix of biology and machine learning makes Dwight well placed to lead WSQ AI for Life Science and Bioinformatics. He guides learners through biological sequence analysis, protein structure and genomic variant detection, and transcriptomics and gene expression analysis, then shows how machine learning can uncover biological patterns and support prediction. Drawing on his dashboard and visualisation work, he also helps participants communicate scientific insights clearly. His applied approach, which weighs the opportunities and risks of AI, prepares learners to use computational methods responsibly in genomics, drug discovery and personalised healthcare research, and to interpret results with genuine biological understanding.