Accounting & Finance FinTech and Financial Intelligence
Accept new applications for new modules in Mar 2024! To become FinTech and Data Analytics professionals, you can learn how to use R and Python to perform Financial Analytics with the practical applications of AI in finance in this programme.
This programme aims to impart finance and investment knowledge and pratical data analytics skills to students to enhance their financial decision making, Contemporary issues in the financial market as well as the latest applications of financial analytics will be discussed. The programme covers computer programming techniques with relevant applications to perform financial analysis and investment management. Teachers are all practitioners in the finance and investment sectors.
1) Mr. W. C. Chan
Mr. W. C. Chan, FRM, has possessed rich experience in financial risk management, information technology and data science and worked as IT Manager over a decade. Being a practitioner in information technology, he is currently a consultant and trainer at Big Data Consultancy Services Company. Also, he is strong in Cloud-based solutions, Big Data Technology, Data Mining and Machine Learning. Moreover, Mr. Chan has obtained a Bachelor Degree in Mathematics from The Chinese University of Hong Kong as well as three Master Degrees in Risk Management Science from The Chinese University of Hong Kong, Quantitative Analysis for Business from City University of Hong Kong and Industrial Logistics Systems from The Hong Kong Polytechnic University.
2) Ms. Isa Kwok
Ms Kwok is a Chartered Management Accountant with over 20 years of post-qualification experience in financial, management accounting and tax planning areas. She had substantial financial analysis and management experiences and held management positions in listed and sizeable organizations and government department. She also has 10 years teaching experiences in accounting and management courses in various local tertiary institutions. Moreover, she has earned a Professional Diploma of Accountancy (ACCA) and a Master of E-Business from City University of Hong Kong, an LLB from the University of London, a Post Graduate of Business Administration from the University of Surrey.
3) Dr. Zenki Kwan
Dr. Zenki Kwan, FRM, CAIA, CB, is the investment director of a listed company and a family office in Hong Kong, responsible for investment strategy and portfolio management across equities, fixed income, currency, funds and structured products. He has previously worked in J.P. Morgan, UBS, McKinsey and Samsung Securities. In addition to his doctoral degree, Dr. Kwan also holds Master of Finance and Master of Applied Business Research degrees as well as completed executive education programs at Harvard Law School and Oxford University Saïd Business School, respectively.
4) Dr. M. K. Lai
Dr. Lai Man-Kit, CFA, is currently a professional trainer at Executive Training and Management Consultancy as well as a visiting scholar at HKUST. Dr. Lai has extensive knowledge in teaching adult continuing education. He was also an Assistant Professor at City University from 1994-2000.
5) Mr. Alan Cheung
Mr. Alan Cheung, PRM, CQF, has solid experience in fintech in top tier investment banks, versed in architecting low latency, high frequency algorithmic trading systems. He is currently a Quantitative Strategist on the Equities Desk in Bank of America Merrill Lynch. Alan has a Master in Mathematical and Computational Finance from the University of Oxford after graduating with First Class Honours in Mathematics with Statistics for Finance from Imperial College London.
6) Mr. Ken Liu
Mr. Ken Liu, co-founder and CTO of Datatact Ltd, a startup focusing 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.
7) 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. He is now also teaching Executive Certificate in Applied Business Analytics and Decision Optimization and Executive Diploma in Financial Analytics in HKU SPACE.
8) Ms. Rowena Lai
Ms. Rowena Lai has extensive experience in business and data analytics in different business sectors. She is currently working in a well-known international bank and leading various data analytics projects. She graduated from the Chinese University of Hong Kong with a Bachelor of Science degree (major in Mathematics and Minor in Economics), and obtained a Master of Science in International Shipping and Transport Logistics as well as a Master of Science degree in Global Supply Chain Management from the Hong Kong Polytechnic University. Ms. Lai is currently a Certified Analytics Professional (CAP) from INFORMS. With her practical experience in data analytics and professional knowledge in financial technology, she teaches "Big Data and FinTech" module under Postgraduate Diploma in Investment Management and Financial Intelligence, Executive Certificate in Big Data and Data Analytics as well as Executive Certificate in Text Analytics and NLP with Financial Technology.
(9) Dr. Garry Luk
Dr. Garry Luk is a Chartered IT Professional (CITP) of British Computer Society. His major research interest in Business system analysis and design, Big data implementation and application, Information management technology. He has been working on the higher education sector for more than 18 years for teaching and research support.
Moreover, Dr. Luk also as a part-time lecturer since 2004 for Vocational training council, School of Continuing Education Hong Kong Baptist University and Hong Kong University of Professional and Continuing Education on E-commerce, Business information system, System Analysis & Design. Dr. Luk will share and inspired Executives at various levels from his rich research and working experiences.
Programme Intended Learning Outcomes:
On completion of the programme, students should be able to:
1. use computational tools to perform and handle data and financial analytics effectively and ethically
2. examine investment products for portfolio optimization and risk diversification
3. apply corporate financial theories for financial management as well as asset valuation and allocation
4. develop financial models using analytical techniques and perform risk analysis
5. analyze financial data for investment analysis and portfolio optimization as well as advise investors in financial decision making
Students who complete all six modules with over 70% attendance and pass all individual assignments and group presentations will be awarded the Postgraduate Diploma in Finance and Data Analytics within the HKU system through HKU SPACE.
|Apply Online Now
|Apply Online Now
Modules & Class Details
Module 1: Data Analytics in Finance using R
- Principles of Data Analytics
- Introduction to R programming
- Statistical Analysis and Data Analytics for finance
- Contemporary issues and development of data analytics
- Applications of data analytics in finance and investment
Module 2: Corporate Financial Management
- Understanding Key Financial Statements
- Financial Analysis Techniques
- Financial Statement Analysis
- Corporate Finance
Module 3: Risk Analysis and Quantitative Asset Allocation
- Principles of Risks Analysis and Financial Risk Management
- Analysis of Financial Risks (Market Risks, Credit Risks, Operational Risks and Integrated Risk Management
- Quantitative Asset Allocation and Portfolio Risk Management
Module 4: Investment Analysis and Portfolio Management
- Analysis and Valuation of investment products
- Portfolio Analysis and Portfolio Management
Module 5: Financial Modeling and Data Analytics
- Data Analytics for Financial Modeling
- Financial Modeling and Forecasting
- Machine Intelligence for Data Analytics and Model Building
Module 6: Financial Data Analytics with Python
- Python for Financial Data Analytics
- Learning Algorithms for Finance
Issues and Applications of AI and Financial Analytics
Fee & Entry Requirements
HK$150 (Non-refundable)Course Fee
- Module fee: $9,800 (* course fees are subject to change without prior notice)
2 installment-:1st- three modules: $29,400 and 2nd- three modules: $29,400
Applicants shall hold a bachelor’s degree in quantitative areas (e.g., mathematics, engineering, statistics, computer science, economics, finance) awarded by a recognised institution.
If the degree or equivalent qualification is from an institution where the language of teaching and assessment is not English, applicants shall provide evidence of English proficiency, such as:
i. an overall band of 6.0 or above with no subtests lower than 5.5 in the IELTS; or
ii. a score of 550 or above in the paper-based TOEFL, or a score of 213 or above in the computer-based TOEFL, or a score of 80 or above in the internet-based TOEFL; or
iii. HKALE Use of English at Grade E or above; or
iv. HKDSE Examination English Language at Level 3 or above; or
v. equivalent qualifications.
Applicants with other qualifications and substantial senior level work experience will be considered on individual merit.
Remark: Applicants with relevant academic and/or professional qualifications may approach the Programme Team for application of exemption.
- The CEF Institution Code of HKU SPACE is 100
|Financial Modeling and Data Analytics (Module from Postgraduate Diploma in Finance and Data Analytics)
|COURSE CODE 33Z129985
|Financial Data Analytics with Python (Module from Postgraduate Diploma in Finance and Data Analytics)
|COURSE CODE 33Z129993
|Corporate Financial Management (Module from Postgraduate Diploma in Finance and Data Analytics)
|COURSE CODE 33Z129977
|Data Analytics in Finance using R (Module from Postgraduate Diploma in Finance and Data Analytics)
|COURSE CODE 33Z129969
|Continuing Education Fund Reimbursable Course (selected modules only)
Some modules of this course have been included in the list of reimbursable courses under the Continuing Education Fund.
Postgraduate Diploma in Finance and Data Analytics
Online Application Apply Now
Application Form Application FormEnrolment Method
Application Form Application Form
We provide online application and payment service for students to make enrolment via the Internet. Enrolment will be confirmed once students have made the payment online by using either PPS or credit card.
For first-come, first-served courses:
- Complete the online application form
Click the "Apply Now" button in the top right-hand corner of the course webpage to make the online application. Follow the instructions to fill in the online application form.
- Make Online Payment
Pay the course fees by either using
PPS via 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.
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For award-bearing programmes:
Selected award-bearing programmes also provide online enrolment and payment service for its students.
If your programme accepts online enrolment and payment, a re-enrolment icon will be shown on the course webpage. Click the icon and follow the instructions to perform online enrolment and payment. You will receive relevant information from the programme team nearer the time of enrolment.
You may click here directly to access the online enrolment and payment service.
Please note the followings:
- Admission is on a first-come, first-served basis. Enrolment will be confirmed once you have made the payment online. You will receive a payment confirmation after payment has been made successfully. 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.
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1. CASH OR EPS
Course fees can be paid by cash or EPS at any HKU SPACE enrolment counters.
2. CHEQUE OR BANK DRAFT
Course fees can also be paid by crossed cheque or bank draft made payable to “HKU SPACE”. Please specify theprogramme 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 payment sent by mail.
Applicants may also pay the course fee by VISA or MasterCard, including the “HKU SPACE MasterCard”, at anyHKU SPACE enrolment centres. Holders of the HKU SPACE MasterCard can enjoy a 10-month interest-freeinstalment 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 (FOR THE COURSE/PROGRAMME HAS ONLINE ENROLMENT ONLY)
The course fees of all open admission courses (course enrolled on first come, first served basis) and selected award-bearing programmes can be settled by using PPS via the Internet. Applicants may also pay the relevant course fees by VISA or MasterCard online. Please refer to the Online Services page on the School website.
For general and short courses, applicants may be required to pay the course fee in cash or by EPS, Visa or MasterCard if the course is to start shortly.
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, 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 transferred 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 approved transfers.
- Receipts will be issued for fees paid but HKU SPACE will not be responsible for any loss of receipt sent by mail.
- For additional copies of receipts, please send a stamped, self-addressed envelope with a completed form and a crossed cheque for HK$30 per copy made payable to ‘HKU SPACE’. Such copies will only normally be issued at the end of a course.
- Relevant Programmes
- Postgraduate Diploma in FinTech and Legal Regulations Postgraduate Diploma in Investment Management and Financial Intelligence Postgraduate Diploma in Investment Management Postgraduate Diploma in Financial Analytics and Algo Trading Executive Diploma in Financial Analytics Executive Certificate in AI and Deep Learning in Quantitative Finance Executive Certificate in Applied Business Analytics and Decision Optimization Executive Certificate in Applied AI and Predictive Analytics for Business Executive Certificate in Applications of Blockchain in Financial Technology Executive Certificate in Big Data, A.I. and Investing Executive Certificate in Interpretation and Visualization of Business Big Data Executive Certificate in Applied Financial Risk Management