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IBF - Data Analytics and Deep Learning for Financial Services

IBF - Data Analytics and Deep Learning for Financial Services

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.

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

Brochure

Download WSQ - Data Analytics and Deep Learning for Financial Services

IBF-STS Accrediation - Up to 70% Funding

Effective for courses starting from 1 Jan 2024
Full Fee	GST	Nett Fee after Funding (Incl. GST)
		Singaporeans below 40 yrs old and PR	Singaporeans above 40 yrs old
$1600	$144.00	$944.00	$624.00

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

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

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.

Course Code: TGS-2022601648

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 - Python Fundamental

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 - Data Analytics and Visualization with Python

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 Basic Deep Learning with Tensorflow

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 Advanced Deep Learning Computational Models

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

Final Assessment

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

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

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

Dr. Alvin Ang: 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: Terence Ee is a technology consultant and trainer specializing in business analytics, financial data modeling, and enterprise IT solutions. With years of experience in data systems integration, business intelligence, and applied analytics, he has trained professionals across banking, finance, and technology sectors on how to leverage data for performance improvement and risk management. His expertise spans Python, SQL, and visualization platforms, enabling him to guide learners in structuring and interpreting financial data for deeper business insights.

As an ACLP-certified trainer, Terence has conducted numerous WSQ and IBF programs in analytics and digital transformation. He focuses on bridging theory with practical applications, teaching learners how to deploy advanced analytics workflows and implement deep learning models to drive financial innovation. His strong industry knowledge, combined with a pragmatic training approach, ensures that participants develop the skills to solve financial sector challenges using data-driven strategies and AI-powered solutions.

Dr. Alfred Ang: 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.