Study Period

The normative study period of part-time students is 2 years.

Curriculum Structure

Students are required to complete not fewer than 75 credits nor more than 84* credits of courses selected from the syllabus which must include capstone experience.

Course Category

No. of Courses

No. of Credits

Discipline Courses
(including 3 Disciplinary compulsory courses)

≥ 8

Not less than 51

Elective Courses 

≤ 2

Not more than 12

Capstone Experience

Project

12

 

Total:

75 to 84 *

* Most courses in the curriculum has 6 credits.  However, courses offered by Faculty of Law has 9 credits.  Candidates who choose one, two, or three 9-credit courses, in addition to the Disciplinary compulsory course in Law, are required to complete 78, 81 or 84 credits respectively for satisfying the curriculum requirement.

Candidates shall select courses in accordance with the regulations of the degree. Candidates must complete a Project and 10 courses with the following requirements.

  1. Candidates must complete at least 3 courses in List-B Disciplinary courses, which must include at least 1 course from List-B-1 and at least 1 course from List-B-2.
  2. Candidates must complete at least 2 courses in List-C Disciplinary courses, which must include at least 1 course from List-C-1 and at least 1 course from List-C-2. 
  3. Candidates may also in exceptional circumstances select at most 2 taught postgraduate level courses (at most 12 credits in total) offered by the Departments in the Faculty of Engineering that are not classified as discipline courses as their elective courses.  All course selection will be subject to approval by the Programme Directors/Course Co-ordinators/Heads of departments concerned.

List-A Disciplinary compulsory courses

            (3 courses)

Discipline

Course

Technology

FITE7409 # Blockchain and Cryptocurrency (6 credits)

or

COMP7408 # Distributed Ledger and Blockchain Technology (6 credits)

Finance

MFIN7002 Investment Analysis and Portfolio Management (6 credits)

Law

LLAW6093 Regulation of Financial Markets (9 credits)

# Candidates holding a non-computer science major should select FITE7409 Blockchain and Cryptocurrency while candidates holding a computer science major should select COMP7408 Distributed Ledger and Blockchain Technology.

List-B Disciplinary courses
              (at least 3 courses)

List-B-1
(with at least one course from List-B-1)

List-B-2
(with at least one course from List-B-2)

COMP7802 Introduction to Financial computing (6 credits)

FITE7407 Securities Transaction Banking (6 credits)

COMP7906 Introduction to Cyber Security (6 credits)

FITE7410 Financial Fraud Analytics (6 credits)

ECOM6016 Electronic Payment Systems (6 credits)

STAT6013 Financial Data Analysis (6 credits)

List-C Disciplinary courses 
              (at least 2 courses)

List-C-1
(with at least one course from List-C-1)

List-C-2
(with at least one course from List-C-2)

FITE7405 Techniques in Computational Finance (6 credits)

COMP7412 Banking in Web3.0 – Metaverse, DeFi, NFTs and beyond (6 credits)

FITE7406 Software Development for Quantitative Finance (6 credits)

ECOM6023 E-financial Services (6 credits)

FITE7801 Topics in Financial Technology (6 credits)

ECOM7126 Machine Learning for Business and E-commerce (6 credits)

COMP7103 Data Mining (6 credits)

FITE7411 RegTech in finance (6 credits)

COMP7305 Cluster and Cloud Computing (6 credits)

IMSE7310 Financial Engineering (6 credits)

COMP7404 Computational Intelligence and Machine Learning (6 credits)

LLAW6046 Privacy and Data Protection (9 credits)

COMP7409 Machine Learning in Trading and Finance (6 credits)

LLAW6126 E-finance: law, compliance and technology challenges (9 credits)

DASC7606 Deep Learning (6 credits)

LLAW6256 Law of Anti-money Laundering and Counter-terrorist Financing and Compliance Issues (9 credits)

STAT8020 Quantitative Strategies and Algorithmic Trading (6 credits)

MFIN7034 ^ Machine Learning and Artificial Intelligence in Finance (6 credits)

or

MFIN7037 ^ Quantitative Trading (6 credits)

STAT6015 Advanced Quantitative Risk Management (6 credits)

^ Candidates can only select either MFIN7034 Machine Learning and Artificial Intelligence in Finance or MFIN7037 Quantitative Trading.

Capstone requirement

FITE7001 Project (12 credits)

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