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Accounting & Finance FinTech and Financial Intelligence

Executive Certificate in Financial Decision Making: Big Data and Machine Learning

Course Code
Application Code
Study mode
Start Date
19 Oct 2024 (Sat)
Next intake(s)
Apr 2025
1 month to 2 months
Course Fee
Course Fee: $9000 per programme (* course fees are subject to change without prior notice)
Deadline on 04 Oct 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 Oct intake! There are practical classes in the computer laboratory. In the Big Data era, Machine Learning (ML), an essential branch of Artificial Intelligence (AI), adopts the scientific study of algorithms and statistical models to improve performance. By using the techniques of ML, data mining and predictive modelling, data analysts will be able to identify hidden relationships, discover new patterns, explore potential opportunities, and thus make better financial decisions. This programme covers popular ML and Predictive Analytics techniques such as Regression Analysis, Decision trees, Random Forest, Naive Bayes, Nearest Neighbors, Neural Networks, K-Means, and Time Series Forecasting. To illustrate more applications, practical cases and issues related to Big Data platforms or model evaluation will be introduced. This programme targets executives who want to acquire knowledge of Big Data and Machine Learning to assist their decision-making. Also, learners who plan to gain an understanding of Data Analytics and apply ML in their workplace are highly welcomed. To handle Big Data, the basics of the programming language will be briefly delivered at the beginning, and our professional lecturer will illustrate various Machine Learning Models with program codes. No advanced statistical knowledge or programming skills are assumed.


This programme aims to provide students with the fundamental concepts and knowledge about Big Data and to develop their analytical skills by applying regression analysis and machine learning to solve business problems. It provides a practical approach for the students to apply regression and machine learning methodologies for analyzing big data and facilitating business and financial decision making.


Programme Details

On completion of the programme, students should be able to:

  • Outline data preparation procedures and examine the process for handling Big Data;
  • Interpret regression results and build business models using regression methods;
  • Apply machine learning methodologies to perform analysis and forecasting;
  • Evaluate various regression and machine learning methods as well as identify patterns for business and financial decision making.


1) Mr. Dexter Ng, a seasoned Financial Risk Manager (FRM) and Chartered Statistician (CStat) with over 7 years of diverse working experience across Banking, Government, and FinTech industries.  With a strong academic background in Statistics and Economics from The University of Hong Kong, Mr. Ng has been able to apply the field of knowledge and run a successful start-up providing data science solutions and consulting services to the Government and SMEs.  With extensive hands-on experience in the application of machine learning, big data, and analytics to real-world solutions, Mr. Ng is passionate about sharing his knowledge and helping students unlock the full potential of data analytics.

2) Ms. Rowena Lai, is a practitioner in Business and Data Analytics. With a Bachelor of Science (Major in Mathematics and Minor in Economics) from The Chinese University of Hong Kong, two Master of Science degrees in both International Shipping and Transport Logistics as well as Global Supply Chain Management from The Hong Kong Polytechnic University, she has worked with different industries on business analytics area.  Ms Lai is currently working in a leading banking and leading various data analytics projects.  She has also worked in an airline industry leader, and shipping industry on Revenue Management and Business Analytics. Thanks to her strong business and analytical sense together with her extended working exposure, she would like to share her academic knowledge and practical experience in Data Science and Analytics.

3) Dr. Roy Wong has more than twenty-five years hands-on experience in design and development of Enterprise Architecture and Software.  He is the Principal Consultant for E-Mars Intelligent Technology LTD now. He is full of enthusiasm in providing professional consulting services and AI related learning course for clients in China, Hong Kong and South Asia.
Dr. Wong received his Doctor of Engineering in The Hong Kong Polytechnic University in 2020. He is a specialist in computer vision system. Dr. Wong has one granted patent and one pending patent in this specific area. Both patents involve the innovation of machine learning. Before obtaining the Doctoral degree, Dr. Wong has five master's degree in Electronic and Information Engineering, Software Technology, Software Engineering, Signal Processing, Business Administration and Psychology. 

Application Code 2250-EP128A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 1:00pm - 7:00pm
  • 30 hours per module
  • Kowloon East Campus
  • Hong Kong Island Campus


Course Content:

(1) Data Preparation for Big Data

  • Data Preparation Process: Data Cleansing, Data Integration, Data Evaluation
  • Import Data
  • Data Cleansing: Handle Missing Values, Recode and Rescale Variables, Separate into Training and Testing Sets
  • Solution for handling Big Data: Hadoop, AWS, Azure


(2) Regression Analysis and Business Model Building

  • Concepts and techniques of regression analysis
  • Assumption Validation and Model Assessment by interpretation of statistical results
  • Issues on analysis of financial Big Data and Cases on business model building


(3) Machine Learning and Forecasting for Big Data

  • Supervised and unsupervised learning approaches: Decision Tree, Regression, Artificial Neural Networks, Cluster Analysis, Association Rule Mining
  • Naïve Bayes Model for Machine Learning
  • Time Series Model for forecasting and model building
  • Multivariate Data Analysis (MDA)
  • Natural Language Processing (NLP): Text Mining, Sentimental Analysis
  • Case study of machine learning for business and financial decision making

Class Details


Oct 2024 intake 

Lecture Date Time
1 19 Oct 24 (Sat) 13:00 - 19:00

26 Oct 24 (Sat)

13:00 - 19:00
3 2 Nov 24 (Sat) 13:00 - 19:00
4 9 Nov 24 (Sat) 13:00 - 19:00
5 16 Nov24 (Sat) 13:00 - 19:00

Remarks: The 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
  • Course Fee: $9000 per programme (* course fees are subject to change without prior notice)

Entry Requirements

Applicants shall hold:

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

Applicants with statistical background are preferred. Those 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.