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WSQ - Data Analytics and Visualization with Python

WSQ - Data Analytics and Visualization with Python

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.

Course Learning Outcomes

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

Course Brochure

Download WSQ - Data Analytics and Visualization with Python Brochure

Skills Framework

This course follows the guideline of Research Data Analysis SSC-DAT-4013-1.1 TSC under Social Service 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-2020504082)

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-2020504082

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: 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

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)
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:

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 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
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 – Dr Alvin Ang is an ACLP-certified trainer with a Ph.D. in Operations Research from Nanyang Technological University and over a decade of academic and industry experience. He has taught at NTU, SUSS, Curtin University, and as an IBM Data Science Instructor, specializing in Python, data analytics, machine learning, and data visualization. As the founder of DataFrens.sg, an open-source data science community, he actively contributes to promoting applied data science and analytics in Singapore.

In his training, Dr Ang focuses on equipping learners with practical skills in Python for data analysis and visualization, using libraries such as Pandas, Matplotlib, and Seaborn. His courses integrate real-world case studies, enabling participants to apply analytical methods to business and research problems. With his structured, learner-centered approach, he ensures learners gain confidence in turning data into actionable insights.

Terence Ee – Terence Ee is an independent consultant and trainer with more than 25 years of experience in IT management, systems integration, and digital transformation. He has held senior positions including Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he led enterprise IT and data-driven innovation projects. Holding a B.Sc. in Computer and Information Sciences from NUS and an M.Sc. in Technology Management from Staffordshire University, Terence blends strategic and technical expertise in his training.

Since 2017, Terence has focused on professional training in Python, data analytics, and visualization. His courses emphasize hands-on learning, guiding participants to use Python tools for data manipulation, statistical analysis, and visualization. By combining real-world business cases with technical exercises, Terence ensures learners develop both the analytical and presentation skills necessary to communicate insights effectively.

Bernard Peh – Bernard Peh is an ACLP-certified principal trainer with over 25 years of experience in data science, finance, and investments. As Senior Partner at BigDataVC, he leverages advanced analytics for financial markets while mentoring professionals and business leaders. He has delivered training across more than eight Accredited Training Organizations in Singapore, reaching over 1,000 learners, and is recognized as a keynote speaker and author in data science and investment analytics.

In his Python and data visualization training, Bernard emphasizes practical applications of data analytics for business and finance. Learners are guided through data preparation, visualization techniques, and dashboard creation using Python libraries, ensuring they can communicate insights clearly and persuasively. His hands-on approach and industry expertise empower participants to apply Python-driven analytics confidently in real-world decision-making.

Solomon Soh – 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 – Shahul Hameed Maricar is an ACLP-certified trainer with over 10 years of experience in software and hardware integration, coding, and data science. With a multidisciplinary background spanning web technologies, embedded systems, and multimedia design, he has conducted numerous WSQ and corporate training programs in Python, web design, and data visualization. His experience also includes supporting IT and multimedia projects at NUS, where he optimized systems integration for research and education.

In his Python data analytics and visualization courses, Shahul emphasizes accessible, hands-on learning tailored for beginners. He guides participants through using Python libraries for data cleaning, analysis, and visualization, ensuring they gain confidence in applying these tools to business and technical problems. His learner-focused approach makes complex topics approachable, enabling professionals to harness Python for data-driven decision-making.