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Accounting & Finance FinTech and Financial Analytics
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》
- Course Code
- EP128A
- Application Code
- 2290-EP128A
- Study mode
- Part-time
- Start Date
- 09 Jun 2025 (Mon)
- Next intake(s)
- Sep 2025
- Duration
- 1 month to 2 months
- Language
- English
- Course Fee
- Course Fee: $9000 per programme (* course fees are subject to change without prior notice)
- Enquiries
-
2867 8331 / 2867 84242861 0278
Today and Upcoming Events
Accept new applications for Jun 25 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.
Programme Overview
Highlights
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.
Teacher
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.
4) Mr. Felix Chan, FRM, ACAMS is a seasoned Regtech professional who has worked at multiple major technology companies, through which he has managed various sectors including finance, government, professional services, etc. He holds a Master of Data Science in the University of Hong Kong and a BSc. in Quantitative Finance and Risk Management at The Chinese University of Hong Kong. Felix is well-equipped with solid compliance, financial and statistical knowledge and focusing primarily on advocating the regulatory technology for enhancing anti-money laundering, third party risk management and trade surveillance.
| Application Code | 2290-EP128A | Apply Online Now |
| Apply Online Now | ||
Days / Time
- Mon, Thu, 7:00pm - 10:00pm
- 30 hours per module
- Kowloon East Campus
- Hong Kong Island Campus
Modules & Class Details
Modules
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
Timetable
Jun 2025 intake
| Lecture | Date | Time |
| 1 | 9 Jun 25 (Mon) | 19:00-22:00 |
| 2 |
12 Jun 25 (Thu) |
19:00-22:00 |
| 3 | 16 Jun 25 (Mon) | 19:00-22:00 |
| 4 | 19 Jun 25 (Thu) | 19:00-22:00 |
| 5 | 23 Jun 25 (Mon) | 19:00-22:00 |
| 6 | 26 Jun 25 (Thu) | 19:00-22:00 |
| 7 | 30 Jun 25 (Mon) | 19:00-22:00 |
| 8 | 3 Jul 25 (Thu) | 19:00-22:00 |
| 9 | 7 Jul 25 (Mon) | 19:00-22:00 |
| 10 | 10 Jul 25 (Thu) | 19:00-22:00 |
Remarks: The tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers.
Articulation Pathways
Progression Path
Fee & Entry Requirements
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:
- a bachelor’s degree awarded by a recognized University or equivalent; or
- 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.
Apply
Online Application Apply Now
Application Form Download Application Form
Enrolment MethodOnline Enrolment
HKU SPACE provides 24-hour online application and payment service for students to apply to selected award-bearing programmes and to enrol in most open admission courses (courses enrolled on a first come, first served basis) via the Internet. Applicants may settle the payment by using either "PPS by Internet" (not available via mobile phones), VISA or Mastercard online. Online WeChat Pay, Online AliPay and Faster Payment System (FPS) are also available for continuing enrolment in the same programme, if online service is offered.
For first time enrolment
-
Complete the online application form
Applicant may click the icon
on the top right-hand corner of the programme/course webpage to make online application, and then follow the instructions to fill in the online application form.
Some programmes/courses may admit by selection, and may require applicants to provide electronic copy of any required documents (e.g. proof of qualification) as indicated on the programme/course webpage. Only file format in doc, docx, jpg and pdf are supported. -
Make Online Payment
Pay the application or programme/course fees by either using:
"PPS by Internet" - You will need a PPS account and a PPS Internet password. For information on how to open a PPS account and how to set up a PPS Internet password, please visit http://www.ppshk.com.
*Credit Card Online Payment - Course fees can be paid by VISA or Mastercard including the “HKU SPACE Mastercard”.
* HKU SPACE Mastercard cardholders who wish to enjoy 10-month interest free instalment scheme must pay their tuition fees in person at any of our HKU SPACE Enrolment Centres.
To know more about first-time online application/enrolment and payment, please refer to the user guide of Online Application / Enrolment and Payment:
For continuing enrolment in the same programme
Selected programmes offer online continuing enrolment service. Programme staff will inform students if they offer this service and offer further enrolment details.
Online Payment can be made via "PPS by Internet" (not available via mobile phones), VISA or Mastercard, Online WeChat Pay, Online AliPay and Faster Payment System (FPS)
In Person / Mail
For first time enrolment
-
For first come, first served short courses, complete the Application for Enrolment Form SF26 and bring or post the completed form(s), together with the appropriate application/course fee(s) and any required supporting documents to any of the HKU SPACE enrolment centres.
[Download Enrolment Form SF26] -
Award-bearing and professional courses may require other information. Forms are usually available at the enrolment centres or on request from programme staff. Bring or post the completed form(s), together with the appropriate application/course fee(s) and any required supporting documents to any of the HKU SPACE enrolment centres.
For continuing enrolment in the same programme
-
The standard ‘Enrolment/Payment Slip’ is designed for students of award-bearing programmes or remaining programmes in a suite of programmes requiring continuing enrolment and it applies to most programmes.
-
Students should complete the “Enrolment/Payment Slip” which will be made available by relevant programme staff and return the slip to any HKU SPACE enrolment centre or post it to the relevant programme staff with appropriate fee payment.
Please refer to available Payment Methods for fee payment information. If you are in doubt about the procedures, please check the individual course details, or contact our programme staff or enrolment centres.
Please note the followings for programme/course enrollment:
- To make an application online, you will need a computer with connection to the Internet and a web browser with JavaScript enabled. Google Chrome is recommended.
- Applicants should not leave the online application idle for more than 10 minutes. Otherwise, applicants must restart the application process.
- Only Early Bird Discount is supported for Online Applicants (Application). To enjoy other types of discount, please visit one of our enrolment centres.
- During the online application process, asynchronous application and payment submission may occur. Successful payment may not guarantee successful application. In case of unsuccessful submission, our programme staff will contact you shortly.
- Applicants are reminded that they should only apply for the same programme/course once through counter or online application.
- For online enrolment, a payment confirmation page would be displayed after payment has been made successfully. In addition, a confirmation email would also be sent to your email account. You are advised to keep your payment confirmation for future enquiries.
- Fees paid are not refundable except as statutorily provided or under very exceptional circumstances (e.g. course cancellation due to insufficient enrolment).
- If admission is by selection, the official receipt is not a guarantee that your application has been accepted. We will inform you of the result as soon as possible after the closing date for application. Unsuccessful applicants will be given a refund of programme/course fee if already paid.
Disclaimer
The School provides a platform for online services for a selected range of products it offers. While every effort is made to ensure timeliness and accuracy of information contained in this website, such information and materials are provided "as is" without express or implied warranty of any kind. In particular, no warranty or assurance regarding non-infringement, security, accuracy, fitness for a purpose or freedom from computer viruses is given in connection with such information and materials.
The School (and its respective employees and subsidiaries) is not liable for any loss or damage in connection with any online payments made by you by reason of (i) any failure, delay, interruption, suspension or restriction of the transmission of any information or message from any payment gateways of the relevant banks and/or third party merchants for processing credit/debit/smart card or other payment facilitation mechanism; (ii) any negligence, mistake, error in or omission from any information or message transmitted from the said payment gateways; (iii) any breakdown, malfunction or failure of those gateways in effecting online payment service or (iv) anything arisen out of or in connection with the said payment gateways, including but not limited to unauthorised access to or alternation of the transmission of data or any unlawful act not permitted by the law.
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 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 personal information and 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 (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, new and continuing students of award-bearing programmes with available online service, they may also pay their course fees by Online WeChat Pay, Online Alipay or Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment for details.
Notes
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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 Enrolment Centres and avoid making cheque payment under this circumstance.
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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, FPS or PPS by Internet will be reimbursed by a cheque, and fees paid by credit card will be reimbursed to the credit card account used for payment.
- 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.
- HKU SPACE will not be responsible for any loss of payment, receipt, or personal information 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.
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