行政人員文憑《金融數據分析》 - 香港大學專業進修學院: FinTech and Financial Intelligence課程
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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
1955-EP148A
Study mode
Part-time
Start Date
06 Nov 2021 (Sat)
Next intake(s)
Jan 2022
Duration
2 months to 4 months
Language
English
Course Fee
$8,600 per module; $17,200 per programme
Apply Now
Deadline on 22 Oct 2021 (Fri)
Enquiries
2867 8331
2861 0278
Accept new application for November intake!

To apply Python programming, Machine Learning and Algorithmic Trading, welcome for enrolling Executive Diploma in Financial Analytics programme. There will be practical classes in computer laboratory.

Python is a high level programming language for tackling data science and computational problems while AI, Machine Learning and Deep Learning are widely using in algorithmic trading system and solving analytical problems.

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.
 

EDFA

 

On completion of the programme, students should be able to

  1. use computer programs to handle financial data and perform financial analytics; (Module 1)
  2. apply mathematical and statistical methods to solve finance and investment problems; (Module 1)
  3. explain financial models and simulations as well as algorithmic trading; (Modules 1 and 2)
  4. examine applications of AI and machine learning to facilitate investment strategies; (Module 2)
  5. analyze financial data and trends to facilitate investment decision making. (Module 2)
Application Code 1955-EP148A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 1:30pm - 7:30pm
Venue
  • Kowloon East Campus
  • Hong Kong Island Campus

Module 1: Python for Financial Analytics (30 hours)

  1. Introduction to Python programming
    1. IDLE environment for Python
    2. Python modules and library
    3. Data Structures, conditional execution and iterations
  2. Mathematics and Statistics for Financial Analytics
    1. Mathematical computation using Python
    2. Statistics using Python
    3. Data Visualization using Python
  3. Applications of Financial Analytics for Modelling and Simulation
    1. Introduction to Financial Analytics
    2. Regression model for predictive analytics
    3. Binomial model for bond and option pricing
    4. Black–Scholes model and option implied volatility
    5. Risk modelling for financial risk management
    6. Monte Carlo Simulation for asset pricing
    7. Simulations using time series models

Assessment method:  In-class exercise + group presentation

 

Module 2: Machine Learning and Algorithmic Trading (30 hours)

  1. AI and Machine Learning
    1. Development of AI and Machine Learning (ML)
    2. Mathematical concepts for Machine Learning
    3. Applications of Machine Learning and Deep Learning: Natural Language Processing, Sentimental Analysis
  2. Learning Algorithms and Models
    1. Supervised Learning: Support Vector Machine, Decision Tree, Random Forest, Regression
    2. Unsupervised Learning: Clustering, Neural Networks, Principal Component Analysis
    3. Reinforcement Learning: Markov Decision Processes, Q-Learning, Policy Gradients
    4. Illustration of computer coding about related algorithms and models for investment
  3. Algorithmic Trading
    1. Investment strategies for algorithmic trading
    2. Trading Execution Algorithms
    3. Strategy Implementation Algorithms
    4. Stealth/Gaming Algorithms
    5. Arbitrage Opportunities
    6. 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
(1) Mr. Kevin Chung
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.

Timetable

Module 1: Python for Financial Analytics

Lecture Date Time
1 6 Nov 21 (Sat) 13:30 - 19:30
2 13 Nov 21 (Sat) 13:30 - 19:30
3 20 Nov 21 (Sat) 13:30 - 19:30
4 27 Nov 21 (Sat) 13:30 - 19:30
5 4 Dec 21 (Sat) 13:30 - 19:30


Module 2: Machine Learning and Algorithmic Trading 

Lecture Date Time
1 Mar 2022 (Sat) 13:30 - 19:30
2 13:30 - 19:30
3 13:30 - 19:30
4 13:30 - 19:30
5 13:30 - 19:30

Remarks : Tentative timetable is subject to change and course commencement is subject to sufficient enrollment numbers.

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

Application Fee

HK$150 (Student only needs to pay one time application fee for all EC in Big Data Series)

Course Fee
  • $8,600 per module; $17,200 per programme

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.

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.