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Accounting & Finance FinTech and Financial Intelligence

Executive Diploma in Financial Analytics

Course Code
Application Code
Study mode
Start Date
13 Jul 2024 (Sat)
Next intake(s)
Oct 2024
2 months to 4 months
Course Fee
$9,200 per module; $18,400 per programme (Course fees are subject to change without prior notice)
Deadline on 28 Jun 2024 (Fri)
2867 8331
2861 0278
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Today and Upcoming Events

Jun 2024

Online Executive Certificate / Diploma Information Seminar - Big Data & FinTech Series (24 Jun 2024)

The recent advances in Big Data and AI have major impact on the investment and trading community.  Now different types of alternative data from news, social sentiment to satellite images can be used to construct and manage investment portfolios. Moreover, Machine Learning is applied to stock price predictions while Reinforcement Learning (Alpha-Go) technique is employed into trading strategies discovery. This programme is suitable for degree holders and Executives who wish to enhance their knowledge and current market practices in the Big Data and FinTech series. Seminar topics: Course details, entry requirements, assessment requirements. This information seminar provides details about: -Executive Diploma in Financial Analytics  行政人員文憑《金融數據分析》 -Executive Certificate in Banking and Financial Technology  行政人員證書《銀行及金融科技》 -Executive Certificate in Big Data and Business Analytics  行政人員證書《大數據與業務分析》 -Executive Certificate in Big Data and Predictive Analytics  行政人員證書《大數據與預測分析》 -Executive Certificate in Big Data, A.I. and Investing  行政人員證書《大數據,人工智能與投資》 -Executive Certificate in Applications of Blockchain in Financial Technology  行政人員證書《區塊鏈在金融科技的應用》 -Executive Certificate in Applied AI and Predictive Analytics for Business  行政人員證書《應用人工智能與商業預測分析》 -Executive Certificate in AI and Deep Learning in Quantitative Finance  行政人員證書《量化投資:人工智能與深度學習》 -Executive Certificate in Applied Business Analytics and Decision Optimization  行政人員證書《應用商業分析與決策優化》 -Executive Certificate in Interpretation and Visualization of Business Big Data  行政人員證書《商業大數據視覺化及資訊演繹》 -Executive Certificate in Financial Decision Making: Big Data and Machine Learning  行政人員證書《財務決策:大數據及機器學習》 -Executive Certificate in Text Analytics and NLP with Financial Technology 行政人員證書《金融科技:文字分析與自然語言處理》 -Certificate for Module (Big Data Governance and Data Compliance)  證書(單元 : 大數據治理及數據合規) -Certificate for Module (Business Analytics and Web Scraping)  證書(單元 : 商業分析及網站擷取) -Certificate for Module (Robotic Process Automation with Business and Financial Applications)  證書(單元:機械人流程自動化於商業與財務應用) -Certificate for Module (Distributed Ledger and Blockchain with Business Applications)  證書(單元 : 分散式帳本與區塊鏈的商業應用) -Certificate for Module (Business Intelligence and Data Automation)  證書(單元 : 商業智能與數據自動化) -Certificate for Module (Business Process Automation with VBA and Python)  證書(單元:商業流程自動化 – VBA及Python) -Certificate for Module (Business Forecasting and Predictive Analytics for Financial Decision Making)  證書(單元:財務決策的商業分析與預測) -Certificate for Module (Technical Analysis and Data Analytics for Stock Investment) 證書(單元 : 股票投資的數據與技術分析) -Certificate for Module (Sustainable Finance and Green FinTech) 證書(單元 : 可持續金融與綠色金融科技) -Certificate for Module (Generative AI, DeFi and Risk Governance) 證書(單元 : 生成式人工智能、去中心化金融與風險管治) -Certificate for Module (Web 3.0 and FinTech) 證書(單元 : 第三代互聯網與金融科技) -Certificate for Module (GenAI and Automation for Finance and Business) 證書(單元 : 生成式人工智能及金融與業務自動化) -Certificate for Module (Financial Data Analytics with Python and Power BI) 證書(單元 : 金融數據分析–Python 及Power BI) -Certificate for Module (AI and ML with Business and Financial Applications) 證書(單元 : 人工智能與機器學習 - 商業與財務應用) -Certificate for Module (Financial Informatics and Data Analytics) 證書(單元 : 金融信息學與數據分析) -Certificate for Module  (Web Application Programming for Finance and Business) 證書(單元:金融與商業網頁應用編程) Unable to join us at the Information Seminar? Email to for One-on-One after-office-hour consultation, by appointment only.

Accept new applications for Jul 2024 intake (Module 1) and accept new applications for Sep 2024 intake (Module 2)! There are practical classes in the computer laboratory. Python is a high-level programming language for tackling data science and computational problems. Investment professionals use Python programming to build financial models and perform financial analytics. Machine Learning algorithms are commonly used in computerized programs. Also, our professional lecturers will discuss the logic and operation of algorithmic trading and the implementation of trading strategies. To apply Python programming, Machine Learning, and Algorithmic Trading, you are welcome to enrol Executive Diploma in Financial Analytics programme.


This programme aims to provide students with the knowledge to investigate financial data which influences finance and investment decisions. Computer coding using Python will be discussed to handle data, build models and perform financial analysis quantitatively. The programme covers the applications of AI, Machine Learning and computerized algorithms to analyze trends and predict financial data.



Programme Details

On completion of the programme, students should be able to

  1. use computer programs to handle financial data and perform financial analytics; (Module 1)
  2. apply mathematical and statistical methods to solve finance and investment problems; (Module 1)
  3. explain financial models and simulations as well as algorithmic trading; (Modules 1 and 2)
  4. examine applications of AI and machine learning to facilitate investment strategies; (Module 2)
  5. analyze financial data and trends to facilitate investment decision making. (Module 2)
Application Code 2235-EP148A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 1:00pm - 7:00pm
  • Kowloon East Campus
  • Kowloon West Campus
  • Hong Kong Island Campus


Module 1: Python for Financial Analytics (30 hours)

  1. Introduction to Python programming
    1. IDLE environment for Python
    2. Python modules and library
    3. Data Structures, conditional execution and iterations
  2. Mathematics and Statistics for Financial Analytics
    1. Mathematical computation using Python
    2. Statistics using Python
    3. Data Visualization using Python
  3. Applications of Financial Analytics for Modelling and Simulation
    1. Introduction to Financial Analytics
    2. Regression model for predictive analytics
    3. Binomial model for bond and option pricing
    4. Black–Scholes model and option implied volatility
    5. Risk modelling for financial risk management
    6. Monte Carlo Simulation for asset pricing
    7. Simulations using time series models

Assessment method:  In-class exercise + group presentation


Module 2: Machine Learning and Algorithmic Trading (30 hours)

  1. AI and Machine Learning
    1. Development of AI and Machine Learning (ML)
    2. Mathematical concepts for Machine Learning
    3. Applications of Machine Learning and Deep Learning: Natural Language Processing, Sentimental Analysis
  2. Learning Algorithms and Models
    1. Supervised Learning: Support Vector Machine, Decision Tree, Random Forest, Regression
    2. Unsupervised Learning: Clustering, Neural Networks, Principal Component Analysis
    3. Reinforcement Learning: Markov Decision Processes, Q-Learning, Policy Gradients
    4. Illustration of computer coding about related algorithms and models for investment
  3. Algorithmic Trading
    1. Investment strategies for algorithmic trading
    2. Trading Execution Algorithms
    3. Strategy Implementation Algorithms
    4. Stealth/Gaming Algorithms
    5. Arbitrage Opportunities
    6. Illustration of computer coding of trading algorithms

Assessment method:  In-class exercise + group presentation

The Executive Diploma will be conferred to candidates who have attained PASS grade and achieved at least 70% attendance of the programme.

Students completing Module 1 can exit the programme with the intermediate award, Executive Certificate in Financial Analytics.
For students completing both Modules 1 and 2, they can get the award of Executive Diploma in Financial Analytics.


(1) Mr. Kevin Chung
Mr. Chung is a specialist in Machine Learning, Statistical Analysis and Data Science. He received his Bachelor and Master Degree in Mathematics from the University of Toronto. He had been a Mathematics and Statistics lecturer in HKUSPACE Community College for more than six years. Since 2013, he became interested and has been doing research in Data Science and Machine Learning. Coming from an academic background, and then working as a machine learning engineer and data scientist, Mr. Chung likes to discuss Data Science and Machine Learning from both theoretical and practical perspectives.

(2) Mr. Ken Liu
Mr. Liu, co-founder and CTO of Datatact Ltd, a startup focus on AI, Machine Learning and Big Data analytics. He is a hands on expert in his specialized area for over 10 years.  Prior to Datatact, Ken worked at Citi, HSBC, Goldman Sachs, Deutsche Bank and Credit Suisse as Algo-Trading developer. Ken earned a Master in Computer Science from USC and a Bachelor in Computer Science from University of Warwick.

(3) Mr. Stephen Cheng
Mr. Stephen Cheng has over 30 Years of experience in the IT industry, with senior positions at international corporations such as Oracle, Hewlett Packard, Digital Equipment Corporation, Compaq Computer, Portal Software, Amdocs. Mr. Cheng’s broad industrial experience ranges from R&D, Software development, Consulting, Marketing, Pre-sales and Professional Services.  Stephen has a strong track record in delivering successful projects worldwide:  Swisscom, Vodafone, China Mobile, Smartone, HSBC, Telstra etc. Mr. Cheng holds a Bachelor of Arts (Physics) from Vassar College; MS and MBA from Rensselaer Polytechnic Institute and Babson College in the US. Mr. Cheng is currently working on a project at the Hong Kong Chinese University, applying Machine Learning and AI techniques on Traditional Chinese Medicine.

(4) Mr. Hong Lin
Mr. Lin has possessed fruitful experience in Fintech development and digital transformation across retail and institutional businesses in Citigroup. Over the past three years, Mr. Lin has acted as an innovator by promoting big data analysis and managing a series of automation projects, covering the full process from streamlining to solution delivery with Automation Anywhere, Python, and VBA. Recently, his primary task is to digitalize the business risk management for the bank’s prime brokerage business with data and automation technologies.
Mr. Lin started his career journey as a business intelligence engineer focusing on Fintech solution development and sales opportunities discovery thru data analysis. In 2017, he engaged in an AI Financial Advisory development by backward engineering trading strategies and analyzing financial news with Natural Language Processing techniques (NLP) in Ping An Securities. In 2018, Mr. Lin led a market research project to optimize product lines thru analyzing more than 100,000 lines of customer reviews on the Internet with web-scraping and NLP. 
Mr. Lin graduated from the University of California, Davis with a Bachelor of Science degree in Managerial Economics Development under Trade and Development of Agricultural Commodities, and the Hong Kong University of Science and Technology with a Master of Science degree in Business Analytics.

(5) Mr. Kevin Leung
Mr. Leung is a seasoned accountant with advanced data analytics and programming skills. He worked at several leading corporations in different industries and supervised teams to drive technological innovation in finance operations. He is also a lecturer, teaching financial analytics and business management courses. He holds an MSc (Distinction) in Business Analytics from the Hong Kong Polytechnic University and a BA (First Class Honours) in Integrated Business and Global Studies from Centennial College. He published a research paper on big data analytics in a reputable journal. Through his professional and academic background, he would like to share his experience in building financial and statistical models by spreadsheet and programming, applying ERP and BI software to data analysis and automating operational processes.

Class Details


Module 1: Python for Financial Analytics

Lecture Date Time
1 13 Jul 24 (Sat)


2 20 Jul 24 (Sat) 13:00-19:00
3 27 Jul 24 (Sat) 13:00-19:00
4 3 Aug 24 (Sat) 13:00-19:00
5 10 Aug 24 (Sat) 13:00-19:00

Module 2: Machine Learning and Algorithmic Trading 

Lecture Date Time
1 7 Sep 24 (Sat) 13:30 - 19:30
2 14 Sep 24 (Sat) 13:30 - 19:30
3 21 Sep 24 (Sat) 13:30 - 19:30
4 28 Sep 24 (Sat) 13:30 - 19:30
5 5 Oct 24 (Sat) 13:30 - 19:30

Remarks: Tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers.


Application Fee

HK$150 (Student only needs to pay one time application fee for all EC in Big Data Series)

Course Fee
  • $9,200 per module; $18,400 per programme (Course fees are subject to change without prior notice)

Entry Requirements

Applicants shall hold:
a)    a bachelor’s degree awarded by a recognized University or equivalent; or
b)   an Associate Degree/ a Higher Diploma or equivalent, and have at least 2 years of relevant working experience.

Applicants with qualifications in quantitative areas (e.g., mathematics, engineering, statistics, computer science, economics, finance) are preferred. 

Applicants with other qualification and substantial senior level work experience will be considered on individual merit.

**Please upload copy of HKID and proof of degree while applying online


Online Application Apply Now

Application Form Download Application Form

Enrolment Method
Payment Method
1. Cash, EPS, WeChat Pay Or Alipay

Course fees can be paid by cash, EPS, WeChat Pay or Alipay at any HKU SPACE Enrolment Centres.

2. Cheque Or Bank draft

Course fees can also be paid by crossed cheque or bank draft made payable to “HKU SPACE”. Please specify the programme title(s) for application and the applicant’s name.. You may either:

  • bring the completed form(s), together with the appropriate course or application fees in the form of a cheque, and any required supporting documents to any of the HKU SPACE enrolment centres;
  • or mail the above documents to any of the HKU SPACE Enrolment Centres, specifying  “Course Application” on the envelope.  HKU SPACE will not be responsible for any loss of payment sent by mail.
3. VISA/Mastercard

Applicants may also pay the course fee by VISA or Mastercard, including the “HKU SPACE Mastercard”, at any HKU SPACE enrolment centres. Holders of the HKU SPACE Mastercard can enjoy a 10-month interest-free instalment period for courses with a tuition fee worth a minimum of HK$2,000; however, the course applicant must also be the cardholder himself/herself. For enquiries, please contact our staff at any enrolment centres.

4. Online Payment

Online application / enrolment is offered for most open admission courses (course enrolled on first come, first served basis) and selected award-bearing programmes. Application fees and course fees of these programmes/courses can be settled by using "PPS by Internet" (not available via mobile phones), VISA or Mastercard. In addition to the aforesaid online payment channels, continuing students of award-bearing programmes, if their programmes offer online service, may also pay their course fees by Online WeChat Pay, Online Alipay and Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment  for details.


  • If the programme/course is starting within five working days, application by post is not recommended to avoid any delays. Applicants are advised to enrol in person at HKU SPACE Enrolement Centres and avoid making cheque payment under this circustance.
  • Fees paid are not refundable except under very exceptional circumstances (e.g. course cancellation due to insufficient enrolment), subject to the School’s discretion. In exceptional cases where a refund is approved, fees paid by cash, EPS, WeChat Pay, Alipay, cheque or PPS (for online payment only) will normally be reimbursed by a cheque, and fees paid by credit card will normally be reimbursed to the payment cardholder's credit card account.
  • In addition to the published fees, there may be additional costs associated with individual programmes. Please refer to the relevant course brochures or direct any enquiries to the relevant programme team for details.
  • Fees and places on courses cannot be transferrable from one applicant to another. Once accepted onto a course, the student may not change to another course without approval from HKU SPACE. A processing fee of HK$120 will be levied on each approved transfer.
  • Receipts will be issued for fees paid but HKU SPACE will not be repsonsible for any loss of receipt sent by mail.
  • For payment certification, please submit a completed form, a sufficiently stamped and self-addressed envelope, and a crossed cheque for HK$30 per copy made payable to "HKU SPACE" to any of our enrolment centres.