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Accounting & Finance FinTech and Financial Intelligence

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
2160-EP128A
Study mode
Part-time
Start Date
06 Apr 2024 (Sat)
Next intake(s)
Nov 2024
Duration
1 month to 2 months
Language
English
Course Fee
Course Fee: $8800 per programme (* course fees are subject to change without prior notice)
Deadline on 22 Mar 2024 (Fri)
Enquiries
2867 8331
2861 0278
Apply Now

Today and Upcoming Events

Accepting applications for Apr intake now! 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.

Highlights

This programme aims to provide students with the fundamental concepts and knowledge about Big Data and to develop their analytical skills by applying regression analysis and machine learning to solve business problems. It provides a practical approach for the students to apply regression and machine learning methodologies for analyzing big data and facilitating business and financial decision making.

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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. 

Application Code 2160-EP128A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 1:00pm - 7:00pm
Duration
  • 30 hours per module
Venue
  • Kowloon East Campus
  • Hong Kong Island Campus

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

Apr 2024 intake 

Lecture Date Time
1 6 Apr 24 (Sat) 13:00 - 19:00
2 13 Apr 24 (Sat) 13:00 - 19:00
3 20 Apr 24 (Sat) 13:00 - 19:00
4 27 Apr 24 (Sat) 13:00 - 19:00
5 4 May 24 (Sat) 13:00 - 19:00

Remarks: The tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers.

Fee

Application Fee

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

Course Fee
  • Course Fee: $8800 per programme (* course fees are subject to change without prior notice)

Entry Requirements

Applicants shall hold:

  1. a bachelor’s degree awarded by a recognized University or equivalent; or
  2. 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 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.