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WSQ - Data Analytics and Visualization with Python

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: Research Data Analysis
TSC Code: SSC-DAT-4013-1.1

Funding for this course is valid from 26 Jun 2020 to 25 Jun 2027. Register and complete the course within this period to qualify for funding support.

After the end of this WSQ Python course, learners will be able to :

LO1: Prepare data for analysis
LO2: Transform data for analysis
LO3: Gain insights from data visualization
LO4: Perform statistical data analysis and time series analysis
LO5: Perform advanced data analysis to interpret results

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 Analytics and Visualization with Python

Course Code: TGS-2020504082

WSQ
SFC
PSEA
UTAP
SFEC
Absentee Payroll
MCES

109 Review(s) | Add Your Review

Expand your knowledge in data analytics and visualization with our WSQ-endorsed course focused on Python. Dive into key topics like data manipulation, statistical analysis, and visualization techniques using Python's powerful libraries. Through practical exercises and real-world examples, you'll learn to transform raw data into actionable insights and visual narratives that are compelling and easy to understand.

By the end of this course, you'll be adept at leveraging Python for advanced data analytics and visualization tasks. Whether you're a data scientist, business analyst, or someone interested in generating insights from data, this course will provide you with the tools and skills to make impactful, data-driven decisions.

Course Objective

Learners will be able to prepare data, perform data analysis and data visualization to gain insights into data.

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: Data Preparation

Data Analytics with Pandas

Pandas DataFrame and Series

Import and Export Data

Filter and Slice Data

Clean Data

Topic 2: Data Transformation

Join Data

Transform Data

Aggregate Data

Topic 3: Data Visualization

Data Visualization with Matplotlib and Seaborn

Visualize Statistical Relationships with Scatter Plot

Visualize Categorical Data with Bar Plot

Visualize Correlation with Pair Plot and Heatmap

Visualize Linear Relationships with Regression

Topic 4: Data Analysis

Statistical Data Analysis

Time Series Analysis

Topic 5: Advanced Data Analytics

Data Piping

Groupby and Apply Custom Functions

Linear Regression

Written Exam
Practical Exam

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)
Basic Python prog language knowledge

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:

Python 3 – free
Visual Studio Code – free
Google Colab – free, runs in the browser with a Google account

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-2020504082.
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
Data Analyst
Business Intelligence Developer
Research Scientist
Machine Learning Engineer
Financial Analyst
Data Visualization Specialist
Marketing Analytics Specialist
Quantitative Researcher
Data Engineer
Bioinformatics Analyst
Operations Research Analyst
Economic Analyst
Predictive Modeler
Sports Analytics Specialist

Trainers

Dr. Alvin Ang is a data scientist and data science educator with a PhD in Operations Research, an MSc in Logistics and a BEng in Mechatronics, Robotics and Automation Engineering, all from Nanyang Technological University. Operations research is the mathematics of better decisions, and his published work on Kanban production-control systems at IEEE conferences reflects the quantitative rigour he brings to every class. He has served as an IBM Data Science Instructor, a Research Fellow at NUS, a Principal Consultant in data science and an Assistant Professor of Mathematics at SP Jain School of Global Management, where he was named Mathematics Professor of the Year, and he lectured undergraduates in statistics and quantitative methods at SUSS. Since 2021 he has trained professionals at Tertiary Infotech in Python, R, SQL, statistics, Excel analytics, Tableau, Power BI, Apache Spark, machine learning and deep learning, and he founded DataFrens.sg, a community for data scientists in Singapore. An ACLP-certified adult educator, he is known for connecting technical concepts to practical, real-world applications, a strength echoed in hundreds of learner recommendations.

In WSQ - Data Analytics and Visualization with Python, Dr. Ang teaches the end-to-end Python workflow he uses as a data scientist: loading and cleaning data with pandas, transforming and summarising it, visualising patterns with Matplotlib and Seaborn, and applying analytical techniques to answer business questions. His operations research background helps learners choose the right method and interpret results correctly, and hands-on exercises on realistic datasets build a repeatable analysis process.

Terence Ee is an experienced IT leader and consultant with more than 25 years of expertise in technology management, information systems, 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 led major IT modernization projects. Since 2017, he has worked as an independent consultant and ACLP-certified trainer, specializing in guiding SMEs and enterprises in adopting future-ready digital platforms. As a WSQ-accredited trainer, Terence delivers programs in digital collaboration, IT governance, and productivity tools. His facilitation emphasizes case studies, simulations, and hands-on practice, enabling learners to apply modern IT systems such as Microsoft 365 and Google Workspace to improve collaboration and business efficiency. With his leadership background and deep IT expertise, Terence prepares learners to thrive in digitally transforming environments.

is a results-driven data scientist and financial services professional with over two decades of experience in data analytics, visualization, and business intelligence. As a Senior Partner and Lead Data Scientist at Big Data Venture Capital, he has led consulting projects, curriculum development, and training operations for blue-chip firms, equipping professionals with practical skills in Microsoft Power BI, Python, and advanced data analytics. His expertise in data acquisition, cleaning, feature engineering, ETL, and visualization ensures that organizations can harness Power BI to transform raw data into actionable insights for strategic decision-making. In addition to his industry leadership, Mr. Peh is a certified adult educator with the WSQ Advanced Certificate in Learning and Performance (ACLP) and has delivered numerous Power BI and data science courses through NTUC LearningHub and other training providers. His ability to merge technical expertise with pedagogy has made him a trusted trainer, empowering professionals to build interactive dashboards, automate reporting workflows, and adopt data-driven cultures. With a unique blend of hands-on technical mastery and proven teaching experience, Mr. Peh has established himself as a credible authority in Power BI and business intelligence solutions

Solomon Soh is a data scientist and AI trainer with extensive experience in Python, data analytics, and visualization. At IBM Singapore, he supervised 24 machine learning and deep learning projects, coaching teams in feature engineering, data cleansing, and ensemble modeling. His professional background includes roles at Workforce Optimizer and Certis Cisco, where he applied predictive analytics and optimization to real-world challenges, delivering measurable business impact.
As a trainer, Solomon has served as lead instructor for data science bootcamps and corporate programs, covering Python, Tableau, and advanced visualization tools. He emphasizes storytelling with data, teaching learners how to transform complex datasets into actionable visual insights. His project-based approach ensures participants not only master technical skills but also develop the ability to present findings effectively to stakeholders.

Shahul Hameed Maricar is a certified ACLP trainer and MOE instructor with over a decade of experience in systems integration, product design, and technical training. His career spans multidisciplinary expertise in 3D art, mechatronics, embedded systems, and industrial design, where he has delivered impactful solutions in both academic and corporate settings. With deep technical skills in 3D design software such as Blender and Fusion 360, as well as programming in Python, JavaScript, and C++, Shahul integrates both digital and physical product development in his teaching.

Review

Customer Reviews (109)

Average Rating: 3.3/5 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

too fast, unable to do the workbook and see the instruction both at the same time. it would be better if it is in person. (Posted on 10/13/2026)

Average Rating: 5.0/5 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

N/A (Posted on 10/13/2026)

Average Rating: 5.0/5 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 great, very helpful and engaging. (Posted on 10/13/2026)

Average Rating: 4.3/5 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

It would be better to include a short lab exercise after each feature or function is introduced, rather than having a single lab session that covers too many features at once, which may make it difficult for learners to complete. (Posted on 9/15/2026)

Average Rating: 4.0/5 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

Lab sessions and learning objects could be lightened. I believe this course as it is, requires an above basic understanding of Python to follow. The addition of all the regression and learning model coding in topic 5 especially, may be a bit hard to follow and the labs and learning sections are a bit of a non sequitur, with the labs occasionally referencing methods that have not yet been taught and the lab objectives themselves are a bit vague.

However, I cannot thank my trainer enough for the work and visible effort he has put in before this course, as well as during the course itself. He did a very good job in explaining concepts as well as making the most of the provided content to provide as approachable and helpful a learning enviroment as possible. (Posted on 9/15/2026)