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IBF - Data Analytics and Deep Learning for Financial Services

IBF - Data Analytics and Deep Learning for Financial Services

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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 1.1 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

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

Customer Reviews (273)

wil recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

More Time (Posted on 3/29/2020)

might recommend Review by Course Participant/Trainee

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Python class was too basic for deep learning. Maybe a bit of linear regression can be taught , as the jump is large . Or offer up reading materials before hand

Justin is a great teacher and is very knowledgeable in the subject (Posted on 3/29/2020)

wil recommend Review by Course Participant/Trainee

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. (Posted on 3/26/2020)

wil recommend Review by Course Participant/Trainee

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. (Posted on 3/19/2020)

wil recommend Review by Course Participant/Trainee

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Please ensure WiFi stability

Ensure notes is updated when given to students (Posted on 3/19/2020)

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. (Posted on 3/19/2020)

wil recommend Review by Course Participant/Trainee

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Provide more practice exercise questions (Posted on 3/19/2020)

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WiFi connection is not stable. Cause difficulties in doing course work (Posted on 3/19/2020)

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. (Posted on 3/17/2020)

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Please provide softcopy notes (Posted on 3/17/2020)

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Softcopy notes
Please correct the typo in mcq quiz question on a.push(). (Posted on 3/17/2020)

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. (Posted on 3/16/2020)

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. (Posted on 3/16/2020)

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. (Posted on 3/15/2020)

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. (Posted on 3/15/2020)

wil recommend Review by Course Participant/Trainee

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2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

. (Posted on 3/15/2020)

wil recommend Review by Course Participant/Trainee

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2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

. (Posted on 3/15/2020)

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. (Posted on 3/14/2020)

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About Us

Tertiary Infotech Academy, established in 2012, is a leading skills-based training provider dedicated to supporting Singapore’s Industry 4.0 transformation. We are a registered Private Education Institute (PEI), offering Diploma and Advanced Certificate programs in Cyber Security and Artificial Intelligence.

As a strong advocate for professional and lifelong learning, we are a WSQ and IBF Approved Training Organisation (ATO). We also serve as an Authorized Training Partner for major global technology and certification bodies, including Microsoft, CompTIA, AWS, Pearson VUE, Autodesk, Linux Foundation.

In addition, our academy is both a Pearson VUE, Kryterion, PSI Authorized Test Centers enabling learners to access industry-recognized examinations in a convenient and professional environment.

Training Grants and Subsidies

WSQ: Company and Singaporeans/PR can apply WSQ 70-90% funding depending on eligibility
SkillsFuture Enterprise Credit (SFEC) - Eligible Singapore-registered companies can tap on $10000 SFEC to cover up to 90% of out-of-pocket expense
SkillsFuture Credit: All Singaporeans aged 25 and above can use their $500 SkillsFuture Credit from the government to pay for a wide range of approved skills-related courses.
IBF-STS Companies and Singaporeans/PR can apply IBF-STS 70-90% funding depending on eligibility
UTAP: NTUC members enjoy 50% subsidy for unfunded course fee support for up to $250 each year when you sign up for courses supported under UTAP
PSEA: Individual can pay using using their Post Secondary Education Account