Course Code: FIN559
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Synopsis

FIN559 Big Data, Cloud Computing and Machine learning provides an overview of computing and cloud technology that brings disruptive changes to various businesses including financial services. It starts with providing a foundational understanding of big data processing framework and approaches, and follows by introducing relevant tools to students through case studies and hands-on training. The course then examines the core elements of cloud computing, the different types of clouds, and the risk and security issues in cloud computing. It provides a framework for selecting the optimal combination of cloud technology to meet business requirements. Furthermore, the course introduces machine learning, covering both supervised and unsupervised algorithms. It discusses biasvariance trade-off for selecting the appropriate models and introduces neural networks and convolutional neural networks. Lastly the course discusses the applications of artificial intelligence and machine learning via case studies.
Level: 5
Credit Units: 5
Presentation Pattern: EVERY JAN

Topics

  • Challenges of big-data computing (the 5 V’s of big data)
  • Big-data computing requirements and framework
  • Programming tool and API for big-data computing
  • Integrating big-data computing in smart solutions
  • Core elements of cloud computing and different types of clouds
  • Security and privacy threats in cloud computing
  • Risk management plan for cloud computing
  • Technical and business models of cloud computing: IaaS, PaaS and SaaS
  • Implementing cloud computing using open source software
  • Introduction to machine learning
  • Supervised learning and unsupervised learning
  • Bias-variance tradeoff
  • Neural networks and convolutional neural networks
  • Artificial intelligence and machine learning applications and case studies

Learning Outcome

  • Appraise big-data computing framework, the challenges in big-data computing issues and the approaches to resolve them
  • Assess different types of clouds, core elements and risk management plans of cloud computing
  • Evaluate IaaS, PaaS and SaaS technology
  • Compare supervised learning against unsupervised learning, and neural networks against convolutional neural networks
  • Construct smart solution based on big-data computing
  • Formulate calculation for practicing cloud economics
  • Implement cloud computing using open source software
  • Design and develop machine learning applications
Part-time Programmes / CET Courses

Date and Duration

DayDateWeekTime
Wednesday28/01/2026319:00 - 22:00
Wednesday04/02/2026419:00 - 22:00
Wednesday11/02/2026519:00 - 22:00
Wednesday25/02/2026719:00 - 22:00
Wednesday04/03/2026819:00 - 22:00
Wednesday11/03/2026919:00 - 22:00


Target Audience

Executive and above who requires knowledge in big data, cloud computing and machine learning.

 

Relevance of Course to employment/upskilling/reskilling

FIN559 Big Data, Cloud Computing and Machine Learning supports employment, upskilling, and reskilling by equipping learners with knowledge and practical skills in adopting cloud-native architectures, big-data platforms, and AI-enabled decision systems. These hands-on exposure to big data frameworks, cloud computing models, and machine learning applications are essential for roles such as data analyst, fintech specialist, cloud solutions analyst, and AI-enabled business analyst. The course also supports the upskilling and reskilling into data- and AI-centric roles in finance, operations, and risk sectors.


Admissions Pre-requisite

Refer to graduate CET admissions and also include Admission Eligibility Criteria for Graduate CET Modular Courses (Admission Eligibility Criteria for Graduate CET Modular Courses).

 

Schedule

TimeAgendaDeliverables
Session 1
  • 19:00 – 20:30
    • Overview of Big Data, Cloud Computing & Machine Learning
    • Challenges of big-data computing (the 5 V’s of big data)
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Data Science and key pioneering big data publications (e.g. File System and MapReduce)
-
Session 2
  • 19:00 – 20:30
    • Big-data computing requirements and framework
    • Programming tool and API for big-data computing
  • 20:30 – 20:45 Break
  • 20:45 - 22:00
    • Introduction of AWS (Amazon Web Services) and Lab Activities
    • AWS Monthly Calculator
-
Session 3
  • 19:00 – 20:30
    • Integrating big-data computing in smart solutions
    • Core elements of cloud computing and different types of clouds
    • Security and privacy threats in cloud computing
  • 20:30 – 20:45 Break
  • 20:45 - 22:00
    • Risk management plan for cloud computing
    • Technical and business models of cloud computing: IaaS, PaaS and SaaS
    • Implementing cloud computing using open source software
-
Session 4
  • 19:00 – 20:30
    • Introduction to machine learning
    • Supervised learning and unsupervised learning
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Bias variance trade-off
-
Session 5
  • 19:00 – 20:30 Neural networks and convolutional neural networks (Part 1)
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Neural networks and convolutional neural networks (Part 2)
-
Session 6
  • 19:00 – 20:30 Artificial intelligence and machine learning applications and case studies (Part 1)
  • 20:30 – 20:45 Break
  • 20:45 - 21:30 Artificial intelligence and machine learning applications and case studies (Part 2)
  • 21:30 - 22:00 In-Class Assessment
In-Class Assessment

 

Assessments

Assignments, Others, PARTICIPATION

 

Trainer info

A/P Bheema Thiagarajan Lokesh is Head of the Electronics Engineering Programme in the School of Science and Technology at SUSS. His teaching and research focus on areas such as machine learning and artificial intelligence, signal processing in communication systems, and technology applications like blockchain. Before joining SUSS, he was a Research Fellow at A*STAR Institute for Infocomm Research (A*STAR I²R), and he has held several academic leadership and teaching roles within SUSS over the years. He holds a PhD from the National University of Singapore and a Bachelor of Technology from the Madras Institute of Technology, and he is active in professional communities such as IEEE.

Dr Liu Fang is a Senior Lecturer in the School of Science and Technology at SUSS. Her work covers topics such as generative AI in education, privacy-preserving methods, cybersecurity, and data analytics, with a practical focus on solving real computing and data problems. Prior to SUSS, she worked in both industry and research roles, including as a Scientist at Sentient.io, and as a Research Fellow at A*STAR Singapore Institute of Manufacturing Technology and at Nanyang Technological University. Dr Liu holds a PhD in Computer Science from Nanyang Technological University, and she has contributed to funded projects that apply data analytics and explainable AI to areas such as trustworthiness assessment and traffic accident analysis.

 

Course Completion requirements

Participants are required to achieve at least 75% attendance and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.

  • Participants are required to complete all surveys and feedbacks related to the course
  • The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
  • Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice

 

Course Fees, payment and refund policy

 International Participants Singapore Citizens (below 40yrs), Permanent Residents Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy1Enhanced Training Support for SMEs2 (Singaporean and PRs)
Course Fees (A) $3,168.00 $2,640.00 $2,640.00 $2,640.00
SSG Grant (70%) (B)  $1,848.00 $1,848.00 $1,848.00
Nett Course fees (A) - (B) = (C) $3,168.00 $792.00 $792.00 $792.00
9% GST on Nett course fees (D) $285.12 $71.28 $71.28 $71.28
Total nett course fees payable including GST (C) + (D) $3,453.12 $863.28 $863.28 $863.28
Less additional funding if eligible under various schemes (F) $-   $-   $528.00 $528.00
Total nett course fees payable including GST, after additional funding form the various schemes (E) - (f) = (H) $3,453.12 $863.28 $335.28 $335.28
1 Mid-Career Enhanced Subsidy: Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.
2 Enhanced Training Support for SMEs: SME-sponsored employees (Singapore citizens and PRs) aged 21 and above may enjoy subsidies up to 90% of the course fees.

 

For the various payment mode, please refer here.
For the refund policy, please refer here. 

 

For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]