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IBF - Data Analytics and Deep Learning for Financial Services

Course Information

  • Sessions 4 days
  • Duration 30 hrs
  • Level Beginner to Intermediate
  • 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.

Skills Framework

TSC Title
Data Analytics and Computational Modelling-4
TSC Code
FSE-DAT-4019-1.1

Funding Validity

Funding for this course is valid from 8 Nov 2021 to 7 Nov 2026. Register and complete the course within this period to qualify for funding support.

Learning Outcomes

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

  • LO1: perform Python coding for data analytics and computational modeling
  • LO2: perform data analytics and visualization to translate insights and patterns embedded in the data
  • LO3: apply basic deep learning techniques using Tensorflow to isolate trends and analyse root causes of issues
  • LO4: apply advanced deep learnings models to uncover relationships between variables

Course Brochure

Download Course Brochure

Certification

  • 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.

About IBF Certification

This course address the following Technical Skills and Competences (TSCs) and proficiency level:  Data Analytics and Computational Modelling FSE-DAT-4019-1.1 Level 4 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 - Data Analytics and Deep Learning for Financial Services

Course Code: TGS-2022601648
  • WSQ
  • SFC
  • PSEA
  • UTAP
  • IBF
  • Absentee Payroll
  • MCES

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What's This Course About

Embark on a transformative learning journey with our IBF-STS Data Analytics and Deep Learning for Financial Services course. Designed for financial professionals and data analysts, this course provides an in-depth understanding of data analytics techniques and deep learning models applicable to the financial services sector. Learn how to harness big data to create predictive models for risk assessment, portfolio optimization, and customer segmentation. By the end of the course, you'll possess the essential skills to analyze complex financial data and generate actionable insights for strategic decision-making.

Dive deeper into the future of financial analytics by mastering deep learning technologies. This course explores advanced techniques like neural networks, natural language processing, and recommendation systems, equipping you with the ability to identify new revenue streams, manage risks, and personalize customer experiences. With practical exercises based on real-world financial data, you'll gain hands-on experience in implementing these advanced analytics solutions, positioning you as a key player in your organization's data-driven transformation.

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

WSQ Funding

Full Fee $1,600.00 Before GST
GST $144.00 9% of fee
Baseline Nett $944.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $624.00 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 FormSubmit PSEA Form

Course FeeBefore Funding

$1,600.00 (GST-exclusive)
$1,744.00 (GST-inclusive)

Course Date

* Required Fields

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

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.

Cancellation & Reschedule Policy

  • 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

Course Details

What You'll Learn

Topic 1.1 Get Started on Python

Overview of Python

Set Python

Code Your First Python Script

Topic 1.2: Data Types

Number

String

List

Tuple

Dictionary

Set

Topic 1.3 Operators

Arithmetic Operators

Compound Operators

Comparison Operators

Membership Operators

Logical Operators

Topic 1.4 Control Structure, Loop and Comprehension

Conditional

Loop

Iterating Over Multiple Sequences

Comprehension

Topic 1.5 Function

Function Syntax

Return Values

Default Arguments

Variable Arguments

Lambda, Map, Filter

Topic 1.6 Modules & Packages

Import Modules and Packages

Python Standard Packages

Third Party Packages

Topic 2.1 Data Preparation

Data Analytics with Pandas

Pandas DataFrame and Series

Import and Export Finance Data

Filter and Slice Finance Data

Clean Missing Data

Topic 2.2 Data Transformation

Create Computed Data Column

Join Finance Data with Concat, Append and Merge

Aggregate Data with Groupby and Pivot Table

Topic 2.3 Data Visualization

Visualize Time Series Data with Line Plot

Visualize Statistical Relationships with Scatter Plot

Visualize Categorical Data with Bar Plot and Pie Plot

Visualize Variation with Box Plot

Visualize Distribution with Histogram

Topic 2.4 Data Analysis

Descriptive Statistics

Rolling Window Average Analysis

Covariance and Correlation

Topic 2.5 Advanced Data Analytics

Apply

Data Piping

Topic 3.1 Introduction to Deep Learning

Overview of Artificial Intelligence (AI) and Deep Learning

Evaluation of Data Analytics Platforms for Deep Learning

Applications of AI to Finance Services

Deep Learning Methodology

Topic 3.2 Neural Network for Regression

What is Neural Network (NN)?

Activation Functions

Mean Square Error (MSE) Loss Function for Regression

Optimization Algorithms

Build a Predictive Regression Model for Sales Forecasting

Topic 3.3 Neural Network for Classification

One Hot Encoding and SoftMax

Cross Entropy Loss Function for Classification

Build a Classification Model for Classifying Currency Notes

Topic 4.1 Image Classification with Convolutional Neural Network (CNN)

Introduction to Convolutional Neural Network (CNN

Build a Image Classification Model for Currency Notes Detection

Small Dataset Overfitting Issue

Methods to Solve Overfitting Issues

Transfer Learning

Topic 4.2 Time Series Forecasting with Recurrent Neural Network (RNN)

Introduction to Recurrent Neural Network (RNN)

LSTM and GRU Models

Build a Time Series Forecasting Model for Stock Price

Assessment

  • 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:

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.

  1. Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
  2. We email you an invoice showing the course fee after funding.
  3. 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.
  4. Click Submit a Claim and search for this course by its course code, TGS-2022601648.
  5. 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).
  6. 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.

  1. Make sure your NTUC membership is paid up before the course starts.
  2. Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
  3. Attend at least 75% of the course and sit all assessments.
  4. 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.
  5. 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).
  6. NTUC usually pays the claim within 4 weeks of submission.

Read more in our UTAP 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.

  1. Check that this course shows the PSEA tag on its MySkillsFuture page.
  2. Check your PSEA balance on MOE's Student Finance System with Singpass.
  3. Download and complete the PSEA withdrawal form, then submit it to us.
  4. 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.
  5. 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.

  1. 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.
  2. Discuss the decision with the assessor or the assessment manager first.
  3. 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.
  4. Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
  5. 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.
  6. You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.

Job Roles

Job Roles

  • Financial Data Scientist
  • Quantitative Analyst
  • Risk Management Analyst
  • Portfolio Manager
  • Investment Banker
  • Credit Analyst
  • Finance Machine Learning Engineer
  • Asset Manager
  • Algorithmic Trader
  • Financial Modeler
  • Finance Business Intelligence Specialist
  • Financial Technology Developer
  • Bank Operations Analyst
  • Hedge Fund Analyst
  • Insurtech Specialist

Trainers

Trainers

Dr. Alfred Ang brings a research-grade quantitative background and current data science credentials to Data Analytics and Deep Learning for Financial Services. 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, and he has published more than 20 peer-reviewed papers and co-invented more than 20 US patents from earlier work in the defence, electronics and semiconductor industries. He later completed a Graduate Diploma in Intelligent Robotics Systems from NUS-ISS and an Executive Programme in Algorithmic Trading from QuantInsti, and his interests have long included machine learning and deep learning. He is a Microsoft Certified Data Scientist (DP-100), AI Engineer Associate (AI-102) and Power BI Data Analyst (PL-300), an AWS Certified AI Practitioner and an IBM certified instructor for the AI Practitioners course. Today he is a full-stack AI developer, Agentic AI solution architect and corporate trainer, and Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, where his team designs Skills Framework-aligned WSQ and IBF programmes. Among the 300+ public repositories on his GitHub is data-mining-ml-labs, a set of interactive Python and Streamlit labs for beginner data mining and machine learning, and n8n-financeadvisor, a stock analysis dashboard with live price and volume charts linked to a Telegram AI agent that explains its Buy, Sell or Hold view. He also holds the Certificate in AI Ethics and Governance (Senior Professional) from NTU.

In this IBF course, Dr. Ang takes financial professionals and data analysts from Python basics to working deep learning models. The opening modules build a solid programming foundation: setting up Python and writing a first script, data types such as numbers, strings, lists, tuples, dictionaries and sets, operators, control structures, loops and comprehensions. From there learners use the core data libraries to load, clean, explore and visualise financial datasets, then build predictive models for problems that matter in financial services, such as risk assessment, portfolio optimisation and customer segmentation. The later sessions introduce neural networks and deep learning, explaining how layers, training and evaluation work and when a deep model genuinely beats a simpler one. His physics and engineering training shows in the way he explains the mathematics intuitively, and his algorithmic trading studies help him choose examples that feel familiar to people who work with markets. He is careful about the limits of models as well, covering overfitting, data leakage and the governance questions that regulators and risk teams ask, informed by his AI ethics certification. As an ACTA-certified trainer and DACE-certified curriculum developer, he teaches through guided, step-by-step labs in the style of his own Streamlit labs, so participants write and run every piece of code themselves and leave with notebooks they can adapt to their own data. To see his data science labs and financial AI projects, explore 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 science and artificial intelligence specialist with extensive experience applying machine learning, deep learning and natural language processing across industries, including finance and healthcare. He holds a PhD and has designed and delivered training in Python programming, data analytics and AI adoption, equipping professionals to harness advanced data-driven methods for strategic decision-making. As a seasoned trainer, he has taught WSQ and IBF-accredited programmes in Python, data analytics and AI, receiving consistent recognition for making advanced technical subjects accessible to professionals. He bridges academic rigour with practical industry applications, from neural networks to predictive modelling.

In IBF - Data Analytics and Deep Learning for Financial Services, Dr. Ang guides financial professionals and data analysts from Python foundations to deep learning. Learners get started with Python, then work through data types, operators, control structures, loops and comprehensions before applying analytics techniques and deep learning models to financial data. His teaching emphasises real-world financial datasets, case studies and industry tools, so participants learn to analyse market patterns, automate risk assessments and build deep learning models that enhance financial insight. By combining research depth with practical delivery, he empowers learners to apply data analytics and AI with confidence in the financial services context.

Terence Ee is a technology consultant and trainer specialising in business analytics, financial data modelling and enterprise IT solutions. His expertise spans Python, SQL and visualisation platforms, and he has trained professionals across the banking, finance and technology sectors to use data for performance improvement and risk management. An ACLP-certified trainer, he has conducted numerous WSQ and IBF programmes in analytics and digital transformation. This builds on more than 25 years in technology management and information systems, including roles as Chief Information Officer at the Supreme Court of Singapore and Vice President of Information Systems at Senoko Energy.

In this IBF course, Terence takes learners from Python fundamentals through to applied deep learning for financial services. Participants begin with data types, operators, control structures, functions and packages, then use Pandas to prepare, transform and visualise finance data, from time-series line plots to histograms, and apply descriptive statistics, rolling averages and correlation analysis. The course then introduces AI and deep learning and their applications in finance. Drawing on his experience of structuring and interpreting financial data for business insight, he links each technique to real questions faced by financial institutions. His pragmatic approach bridges theory and practice, so learners finish with working analytics workflows and the confidence to apply data-driven, AI-powered methods to financial sector challenges.

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