WSQ - Python Programming for Finance

Topic 1: Introduction to Python Programming

• Business requirements and objectives
• Applications of Python programming to meet business requirements
• Install Python and Setup Python IDE

Topic 2: Data Types and Operators

• Data Types
• Operators

Topic 3: Problem Solving with Control Structures

• Problem solving with conditional and loop techniques
• Coding using comprehensions

Topic 4: Scripting with Function and Lambda

• Create Python functions to meet business use cases
• Lambda function and its applications

Topic 5: Import and Process Finance Data

• Data analysis using Pandas package
• DataFrame and Series data structures
• Import finance data
• Filter and slice finance data
• Clean missing data

Topic 6: Aggregate and Visualize Finance Data

• Join finance data with concat, append and merge
• Aggregate data with groupby and pivot table
• Assess codes to identify gaps
• Test and visualize finance data

Topic 7: Analyze Finance Data

• Improve codes with pipe and apply
• Applications of statistics
• Analyse finance data to track any changes

Final Assessment

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

Promotion Code

Promo or discount cannot be applied to WSQ courses

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.

Minimum Software/Hardware Requirement

Software:

Download and Install the following software

• Python
• Visual Studio Code
• Anacoda

Sign up free Google Colab account

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

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

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

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

1. The candidate has the right to disagree with the assessment decision made by the assessor.
2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
6. 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.
7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
9. The assessor can help the candidate with writing and lodging the appeal.
10. he assessment manager will collect information from the candidate & assessor and give a final decision.
11. A record of the appeal and any subsequent actions and findings will be made.
12. An Assessment Appeal Panel will be formed to review and give a decision.
13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
15. Please click the link below to fill up the Candidates Appeal Form.

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Dr. Alvin Ang: Dr. Alvin Ang is an ACLP-certified trainer with a Ph.D. in Operations Research from Nanyang Technological University and extensive teaching experience in Python, quantitative methods, and finance analytics. He has served as a lecturer at universities such as NTU, SUSS, and Curtin, and as a Data Science/AI trainer at Tertiary Infotech. With multiple IBM and Kaggle certifications in Python, R, data science, and machine learning, Alvin combines academic rigor with practical applications in finance, operations, and analytics.

In his Python for Finance training, Alvin emphasizes applying Python to financial modeling, portfolio analysis, and risk assessment. Learners gain hands-on practice with Python libraries such as pandas, NumPy, and matplotlib to perform time-series analysis, data visualization, and quantitative finance computations. His structured approach ensures participants understand both the technical coding aspects and the financial logic behind their applications.

Terence Ee: Terence Ee is an ACLP-certified trainer and seasoned IT executive with more than 25 years of leadership experience in enterprise systems, data management, and technology operations. He previously served as Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy, where he implemented large-scale IT and reporting systems. Now an independent consultant and trainer, he delivers WSQ and corporate programs in IT, data analytics, and applied business technologies.

In his Python for Finance training, Terence focuses on bridging programming with business and organizational needs. He introduces learners to Python for data cleaning, visualization, and financial reporting, guiding them to apply analytics for decision-making. With his executive background, he contextualizes Python coding exercises in scenarios such as financial dashboards, trend analysis, and operational reporting, making the training practical and relevant for professionals.

Solomon Soh: Solomon Soh is a data scientist and ACLP-certified trainer with strong expertise in Python, machine learning, and finance-related applications. At IBM Singapore and Workforce Optimizer, he managed projects involving financial forecasting, optimization models, and AI-powered decision support systems. His technical expertise spans Python (pandas, NumPy, scikit-learn), Flask, SQL, and deployment pipelines for financial and analytics solutions.

In his Python for Finance training, Solomon emphasizes practical coding and problem-solving. He teaches learners to build Python scripts for financial data extraction, time-series forecasting, and portfolio simulations. By incorporating real-world case studies and hands-on projects, he ensures participants develop the confidence to apply Python in solving finance and investment challenges.

Yeoh Heng Theng: Yeoh Heng Theng is a data science and analytics professional with extensive experience in Python programming, machine learning, and quantitative modeling. As the Data & Analytics Product Lead at Amplify Health, she oversees AI-driven projects that enhance financial and business intelligence across regional operations. Her career spans key roles in companies such as Huawei International and DataRobot, where she developed and implemented data analytics solutions for enterprise clients. Holding a Master of Technology in Enterprise Business Analytics from the National University of Singapore, she brings both technical expertise and strategic acumen to data-driven decision-making.

In “Python Programming for Finance,” Heng Theng focuses on helping learners apply Python to real-world financial analytics and quantitative modeling. Her sessions cover financial data manipulation, statistical analysis, and portfolio optimization using libraries such as NumPy, pandas, and scikit-learn. By blending finance principles with data science techniques, she equips participants to automate workflows, build forecasting models, and extract actionable insights from complex financial datasets.

Mohamed Afiq: Mohamed Afiq is a financial data analyst and programming instructor with expertise in Python development, financial modeling, and data visualization. With a background in financial engineering and quantitative analytics, he has applied programming solutions to support risk assessment, market prediction, and investment decision-making. As an educator, Afiq specializes in simplifying technical concepts and guiding learners in developing practical coding proficiency for finance applications.

In “Python Programming for Finance,” Afiq teaches participants how to harness Python’s capabilities for automating financial tasks and conducting analytical modeling. His training covers topics such as time-series forecasting, data cleaning, and visualization using libraries like matplotlib and seaborn. Through real-world exercises and case-based learning, he enables learners to develop Python-based solutions for pricing, trading analysis, and financial performance evaluation, bridging programming knowledge with applied financial insight.