登入成功 
Accounting & Finance FinTech and Financial Intelligence
Executive Diploma in Financial Analytics
行政人員文憑《金融數據分析》
- Course Code
- EP148A
- Application Code
- 2190-EP148A
- Study mode
- Part-time
- Start Date
- 08 Jun 2024 (Sat)
- Next intake(s)
- Jul 2024
- Duration
- 2 months to 4 months
- Language
- English
- Course Fee
- $9,000 per module; $18,000 per programme (Course fees are subject to change without prior notice)
- Enquiries
-
2867 83312861 0278
Today and Upcoming Events
Accept new applications for Jun 2024 intake (Module 1) and 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.
Programme Overview
Highlights
| 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
|
| Application Code | 2190-EP148A | Apply Online Now |
| Apply Online Now | ||
Days / Time
- Saturday, 1:00pm - 7:00pm
- Kowloon East Campus
- Hong Kong Island Campus
Modules & Class Details
Modules
Module 1: Python for Financial Analytics (30 hours)
- Introduction to Python programming
- IDLE environment for Python
- Python modules and library
- Data Structures, conditional execution and iterations
- Mathematics and Statistics for Financial Analytics
- Mathematical computation using Python
- Statistics using Python
- Data Visualization using Python
- Applications of Financial Analytics for Modelling and Simulation
- Introduction to Financial Analytics
- Regression model for predictive analytics
- Binomial model for bond and option pricing
- Black–Scholes model and option implied volatility
- Risk modelling for financial risk management
- Monte Carlo Simulation for asset pricing
- Simulations using time series models
Assessment method: In-class exercise + group presentation
Module 2: Machine Learning and Algorithmic Trading (30 hours)
- AI and Machine Learning
- Development of AI and Machine Learning (ML)
- Mathematical concepts for Machine Learning
- Applications of Machine Learning and Deep Learning: Natural Language Processing, Sentimental Analysis
- Learning Algorithms and Models
- Supervised Learning: Support Vector Machine, Decision Tree, Random Forest, Regression
- Unsupervised Learning: Clustering, Neural Networks, Principal Component Analysis
- Reinforcement Learning: Markov Decision Processes, Q-Learning, Policy Gradients
- Illustration of computer coding about related algorithms and models for investment
- Algorithmic Trading
- Investment strategies for algorithmic trading
- Trading Execution Algorithms
- Strategy Implementation Algorithms
- Stealth/Gaming Algorithms
- Arbitrage Opportunities
- 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.
Teacher
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
Timetable
Module 1: Python for Financial Analytics
| Lecture | Date | Time |
| 1 | 8 Jun 24 (Sat) |
13:00-19:00 |
| 2 | 15 Jun 24 (Sat) | 13:00-19:00 |
| 3 | 22 Jun 24 (Sat) | 13:00-19:00 |
| 4 | 29 Jun 24 (Sat) | 13:00-19:00 |
| 5 | 6 Jul 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.
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- $9,000 per module; $18,000 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.
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 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.
Notes
- 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.
- More Programmes of
- FinTech and Financial Intelligence
- Relevant Programmes
- Executive Certificate in Financial Decision Making: Big Data and Machine Learning Executive Certificate in Interpretation and Visualization of Business Big Data Executive Certificate in Big Data and Business Analytics Executive Certificate in Big Data and Predictive Analytics Executive Certificate in Big Data, A.I. and Investing Executive Certificate in Applications of Blockchain in Financial Technology Executive Certificate in Applied AI and Predictive Analytics for Business Executive Certificate in Applied Business Analytics and Decision Optimization Postgraduate Diploma in Finance and Data Analytics
