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IBF - AI Assisted Python Programming for Finance

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: Programming and Coding-3
TSC Code: FSE-DIT-3018-1.1

Funding for this course is valid from 31 Jan 2025 to 30 Jan 2030. Register and complete the course within this period to qualify for funding support.

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

LO1: Gather and interpret business requirements of software through business use cases
LO2: Review and propose overall coding best practices to guide individuals in executing coding
LO3: Review and detect errors and areas for improvement within written codes and communicate accordingly
LO4: Test completed codes to identify gaps and possible improvement areas
LO5: Manage programming and coding activities and monitor outputs
LO6: Group coding outputs using a systematic approach
LO7: Ensure programming and coding documentation is accurate and complete

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.

This course address the following Technical Skills and Competences (TSCs) and proficiency level: FSE-DIT-3018-1.1 Programming and Coding-3 TSC under Financial Services Skills Framework

Participants are encouraged to access the IBF MySKills Portfolio https://www.ibf.org.sg/home/for-individuals/resource-tools/myskills-portfolio to track their training progress and skills acquisition against the Skills Framework for Financial Services. You can apply for IBF Certification after fulfilling the required number of Technical Skills and Technical Competencies (TSCs) for the selected job role.

Find out more about IBF certification and the application process at on https://www.ibf.org.sg/home/for-individuals/ibf-certification/why-be-ibf-certified.

IBF - AI Assisted Python Programming for Finance

Course Code: TGS-2025052659

WSQ
SFC
PSEA
UTAP
IBF
Absentee Payroll
MCES

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This comprehensive course on Python Programming for Financial Services is designed from basic to advanced levels, targeting professionals looking to leverage Python for financial data analysis and software development within the finance sector. Participants will start with an introduction to Python programming, understanding its application in meeting business requirements, setting up Python IDEs, and diving deep into Python data types and operators. The course emphasizes practical problem-solving with control structures, scripting with functions and lambda expressions, and effective error handling techniques to manage exceptions.

As the course progresses, learners will gain hands-on experience in importing, processing, aggregating, and visualizing financial data using powerful Python libraries like Pandas. Key topics include object-oriented programming to enhance code efficiency, analyzing finance data with advanced statistical methods, and applying best coding practices for error detection and improvement areas. With a focus on real-world finance applications, this course ensures participants can confidently manage programming activities, document coding processes accurately, and apply systematic approaches to group coding outputs, preparing them for a successful career in the financial services industry..

This course is part of our AI training in Singapore, where you can browse all our AI courses. See more AI vibe coding 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 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: 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: Error Handling Using Exception

Exceptions versus Syntax Errors

Handle Exceptions with Try and Except blocks

The Else clause

Clean up with Finally

Topic 6: 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 7: 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 8: Object Oriented Programming

Introduction to Object Oriented Programming

Create Class and Objects

Method and Overloading

Initializer & Destructor

Inheritance

Polymorphism

Topic 9: Analyze Finance Data

Improve codes with pipe and apply

Applications of statistics

Analyse finance data to track any changes

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:

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.

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-2025052659.
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 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

Financial Data Analyst
Python Developer for Financial Services
Finance Software Engineer
Quantitative Analyst
Financial Systems Analyst
Risk Management Specialist
Investment Analyst
Data Scientist in Finance
Algorithmic Trading Developer
Financial Application Developer
Fintech Developer
Portfolio Manager
Credit Risk Analyst
Financial IT Project Manager
Treasury Analyst
Compliance Analyst
Financial Planning and Analysis Analyst
Market Research Analyst
Business Intelligence Analyst in Finance
Financial Operations Analyst

Trainers

Finance and Python meet in several of Dr. Alfred Ang's own projects, and he brings both an engineer's discipline and a grounding in quantitative trading to this IBF course. He has completed an Executive Programme in Algorithmic Trading from QuantInsti, and 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. A full-stack AI developer and Agentic AI solution architect, he serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, where his teams design Skills Framework-aligned WSQ and IBF programmes. His data credentials include Microsoft Certified Data Scientist (DP-100) and Power BI Data Analyst (PL-300), AWS Certified AI Practitioner and IBM certified instructor for the AI Practitioners course, and he has published more than 20 peer-reviewed papers. Among his 300+ public GitHub repositories, n8n-financeadvisor is a stock analysis dashboard with TradingView and TwelveData charts showing live OHLC and volume data, paired with an AI agent that delivers Buy, Sell or Hold recommendations with its reasoning. He has also built data-mining-ml-labs, interactive Python and Streamlit labs for beginners, a financial planning website with a retirement calculator, and an HR and accounting platform for his own company that handles Singapore CPF payroll. He is a member of the Singapore Computer Society and has co-invented more than 20 US patents, reflecting a long record of applying technical problem-solving in industry.

AI Assisted Python Programming for Finance takes finance professionals from their first lines of Python to analysing real financial datasets, with an AI coding assistant supporting every step. Dr. Ang begins with business requirements and how Python meets them, setting up the IDE, then works through data types, operators and problem solving with conditional statements, loops and comprehensions. Learners write functions and lambda expressions for finance use cases and learn to handle errors properly with try, except, else and finally blocks. The second half focuses on data: importing, filtering, slicing and cleaning finance data with the Pandas DataFrame and Series structures; joining datasets with concat and merge; aggregating with groupby and pivot tables; and testing and visualising the results. Object-oriented programming follows, covering classes, objects, inheritance and polymorphism, and the course ends with improving code using pipe and apply and applying statistics to track changes in financial data. He uses the AI assistant as a tutor that explains, reviews and suggests improvements, while insisting that learners verify every calculation, a habit that matters greatly in finance. As an ACTA-certified trainer and DACE-certified curriculum developer, he teaches through short demonstrations and plenty of guided practice on finance examples, so learners gain usable skills rather than syntax alone. Learners leave able to automate routine analysis in their own roles. To explore his finance and data projects, visit his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/

Dr. Alvin Ang is a data scientist and educator with a PhD in Operations Research, specialising in quantitative methods, machine learning and applied data analytics. He has extensive experience in research and teaching, combining academic rigour with practical applications in data-driven decision-making. At Tertiary Infotech, he has designed and delivered WSQ courses in data science, Python programming and AI, equipping learners to transform raw data into actionable insights. As an ACLP-certified trainer, he focuses on applied learning, using real-world case studies and hands-on exercises to build both conceptual understanding and technical proficiency in data visualisation, predictive modelling and AI techniques.

In IBF - AI Assisted Python Programming for Finance, Dr. Ang brings this Python and AI teaching experience to finance professionals who want to use Python for financial data analysis and software development. Learners start with how Python programming can meet business requirements, set up a Python IDE, and then work through data types and operators, problem solving with conditional and loop techniques, comprehensions and scripting with functions. Throughout, he shows how AI assistance can speed up coding while learners stay in control of the logic. By bridging theory and practice, he helps participants leave able to write reliable Python scripts that support analysis and decision-making in financial services.

Noel Lou is a financial technology professional and data analytics educator with deep expertise in quantitative analysis, financial systems and programming for business applications. He has extensive experience implementing analytics solutions and automation tools for the finance and banking sectors, using Python, SQL and Excel VBA to optimise data workflows and reporting accuracy. As an ACLP-certified trainer, Noel has designed and delivered professional courses in data analytics, programming and digital transformation for public and corporate clients, and he is known for translating complex technical concepts into simple, practical lessons.

In IBF - AI Assisted Python Programming for Finance, Noel teaches learners how to apply Python to streamline financial operations and derive insights from data. Learners start with an introduction to Python programming and how it meets business requirements, setting up Python and an IDE, then cover data types and operators, problem solving with control structures and comprehensions, and scripting with functions. His structured, hands-on approach builds confidence in coding fundamentals, data cleaning and visualisation with libraries such as Pandas and Matplotlib. Learners complete real-world exercises that simulate financial analysis and automation tasks, equipping them to improve data accuracy and efficiency in financial environments.

Teddy Yip Fook Khi is a business consultant and corporate trainer with over 25 years of experience in financial management, process improvement and digital transformation. He holds a Master of Science in Finance and Controlling and is a WSQ Advanced Certificate in Learning and Performance (ACLP) certified trainer. Teddy has delivered training in data analytics, financial modelling and Python programming for business professionals across various industries, and his expertise lies in combining financial analysis with automation and data-driven decision-making to help organisations modernise their workflows.

In AI Assisted Python Programming for Finance, Teddy helps learners apply Python to real-world financial problems. Participants start by linking Python to business requirements, installing Python and setting up an IDE, then work with data types and operators. They solve problems with conditional and loop techniques and comprehensions, write functions and lambda expressions for business use cases, and handle exceptions robustly. Throughout, his sessions emphasise hands-on coding and analytical exercises that bridge financial concepts with programming fundamentals, such as data manipulation, forecasting and performance analysis. Learners gain the practical skills to automate reporting, analyse datasets and improve efficiency within financial operations.

Terence Ee is an 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 as Vice President of Information Systems at Senoko Energy, where he led major IT modernisation projects. Since 2017 he has worked as an independent consultant and ACLP-certified trainer, guiding SMEs and enterprises in adopting future-ready digital platforms. As a WSQ-accredited trainer, he delivers programmes in digital collaboration, IT governance and productivity tools, and his facilitation is built on case studies, simulations and hands-on practice.

In this IBF course, Terence brings that information systems background to the practical side of Python programming for financial services. Having spent his career aligning technology with organisational needs, he helps learners start from business requirements and then work step by step through data types, control structures, functions and lambda expressions, and exception handling. Learners go on to import, clean, aggregate and visualise finance data with Pandas, apply object-oriented programming, and analyse finance data to track changes over time. His hands-on, case-based approach keeps each exercise tied to a realistic business use case, so participants leave able to write, test and improve their own Python scripts for finance data.