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IBF STS Funded 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.

Learning Outcomes

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

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

Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.
  • 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.

IBF - Data Analytics and Deep Learning for Financial Services

Course Code: TGS-2022601648
  • WSQ
  • SkillsFuture Credit
  • PSEA
  • UTAP
  • IBF
  • SFEC
  • Absentee Payroll
  • MCES

273 Review(s) | Add Your Review

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.

Brochure

Download WSQ - Data Analytics and Deep Learning for Financial Services

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/programmes/Pages/MySkills-Portfolio.aspx 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/certification/Pages/Why-be-Certified.aspx.

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
IBF-STS Accrediation - Up to 70% Funding
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

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

Promotion Code

Promo or discount cannot be applied to IBF-STS 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.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Softtware: Windows / Mac

Hardware: Laptop

Self-Sponsored Individuals

  • Up to 70% subsidy is available for Singapore Citizens and Permanent Residents of Singapore, physically based in Singapore. GST funding support will no longer be applicable for all courses.

Company-Sponsored Individuals

  • Up to 70% subsidy is available for Singapore Citizens and Permanent Residents of Singapore, physically based in Singapore. Please note:
  • The company must be a Financial Institution regulated by MAS or a FinTech firm certified by Singapore FinTech Association (SFA)
  • To register, please email your company name and your name to reachus@knowledgehut.com.sg.
  • For more information on the IBF subsidies and eligibility, please visit:  https://www.ibf.org.sg/programmes/Pages/IBF-STS.aspx

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’

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.

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. Alvin Ang is a data science and artificial intelligence specialist with extensive experience in applying machine learning, deep learning, and natural language processing across industries, including finance and healthcare. He has designed and delivered training in Python programming, data analytics, and AI adoption, equipping professionals with the skills to harness advanced data-driven methods for strategic decision-making. With a PhD in Computer Science and strong research expertise, Dr. Ang bridges academic rigor with practical industry applications, enabling learners to master complex concepts such as neural networks, predictive modeling, and algorithmic optimization in the financial services context.
As a seasoned trainer, Dr. Ang has taught WSQ and IBF-accredited programs in Python, data analytics, and AI, receiving consistent recognition for making advanced technical subjects highly accessible to professionals. His teaching emphasizes real-world financial datasets, case studies, and industry tools, ensuring participants gain the ability to analyze market patterns, automate risk assessments, and develop deep learning models to enhance financial insights and innovation. By combining research depth, technical expertise, and practical delivery, Dr. Ang empowers learners to excel in applying data analytics and AI within financial services.

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.

Dr. Alfred Ang is a distinguished technologist, educator, and Chief Instructional Designer at Tertiary Infotech, with over 20 years of experience in artificial intelligence, data analytics, and financial technology. As CTO, he has led the design of over 500 accredited programs across AI, machine learning, and cloud computing, empowering professionals to adopt data-driven strategies in business and finance. His expertise spans predictive analytics, algorithmic modeling, and deep learning applications, particularly in the context of financial forecasting, risk assessment, and fraud detection.
In “Data Analytics and Deep Learning for Financial Services,” Dr. Ang guides participants in applying modern AI and data science techniques to real-world financial challenges. His sessions focus on using Python and TensorFlow to develop predictive models, automate financial analysis, and extract insights from large-scale transactional data. By combining theoretical depth with hands-on implementation, he equips learners with the technical and analytical skills to harness deep learning for smarter, faster, and more accurate decision-making in the financial domain.

Review

Customer Reviews (273)

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