Postgraduate Diploma in Finance and Data Analytics (CEF) - HKU SPACE: Investment Management, FinTech and Financial Intelligence courses
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Accounting & FinanceInvestment Management

Postgraduate Diploma in Finance and Data Analytics
金融及數據分析深造文憑

CEF Reimbursable Course (selected modules only)

CEF Reimbursable Course (selected modules only)

New Course
Course Code
FN050A
Application Code
1845-FN050A

Study mode
Part-time
Start Date
10 Nov 2020 (Tue)
Next intake(s)
Jan 2021
Duration
1 year to 2 years
Language
English
Course Fee
Module fee: $9,000 (* course fees are subject to change without prior notice)
2 installment-:1st- three modules: $27,000 and 2nd- three modules: $27,000
Apply Now
Deadline on 27 Oct 2020 (Tue)
Enquiries
2867 8476
2861 0278
Accept new application for Nov 2020 intake! 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.

The programme covers a wide range of knowledge in Statistical Analysis, Corporate Financial Management, Risk Analysis and Asset Allocation,  as well as Investment and Portfolio Management, Financial Modelling and Analytics Software. Students will learn how to apply the knowledge and techniques used by market professionals and refresh their investment knowledge using big data.

Modules

Programme Objectives:

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.

 

Teachers:

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.

 

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

Award:

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.

Application Code 1845-FN050A Apply Online Now
Apply Online Now

Venue

Modules:

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

Class Schedule

Remark: Tentative timetable is subject to change and module commencement is subject to sufficient enrollment numbers.

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.

Application Fee

HK$150 (Non-refundable)

Course Fee
  • Module fee: $9,000 (* course fees are subject to change without prior notice)
    2 installment-:1st- three modules: $27,000 and 2nd- three modules: $27,000
  • The CEF Institution Code of HKU SPACE is 100
CEF Courses
new Financial Modeling and Data Analytics (Module from Postgraduate Diploma in Finance and Data Analytics)
COURSE CODE 33Z129985 FEES $9,000 ENQUIRY 2867-8476
new Financial Data Analytics with Python (Module from Postgraduate Diploma in Finance and Data Analytics)
COURSE CODE 33Z129993 FEES $9,000 ENQUIRY 2867-8476
new Corporate Financial Management (Module from Postgraduate Diploma in Finance and Data Analytics)
COURSE CODE 33Z129977 FEES $9,000 ENQUIRY 2867-8476
new Data Analytics in Finance using R (Module from Postgraduate Diploma in Finance and Data Analytics)
COURSE CODE 33Z129969 FEES $9,000 ENQUIRY 2867-8476
Continuing Education Fund Reimbursable Course 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

  • This course is recognised under the Qualifications Framework (QF Level [6])

Online Application Apply Now

Application Form Application Form

Enrolment Method

Application Form Application Form

Enrolment Method

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:
  1. 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.
  2. 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.

    Credit Card Online Payment - Course fees can be paid by VISA or MasterCard via a secure online payment gateway for all first-come, first-served courses.

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:

  1. 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.
  2. Fees paid are not refundable except as statutorily provided or under very exceptional circumstances.
  3. To make an application online, you will need a computer with the connection to the Internet and a web browser with JavaScript enabled. Internet Explorer 5.01 or above is recommended as the web browser.

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.

Payment Method

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.
3. VISA/MASTERCARD

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.

Notes

  1. 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.

  2. 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.

  3. 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.
  4. 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.
  5. Receipts will be issued for fees paid but HKU SPACE will not be responsible for any loss of receipt sent by mail.
  6. 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.