Executive Certificate in Financial Decision Making: Big Data and Machine Learning - HKU SPACE: FinTech and Financial Intelligence courses
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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》

New Course
Course Code
EP128A
Application Code
1755-EP128A
Study mode
Part-time
Start Date
16 Nov 2019 (Sat)
Next intake(s)
Jan 2020
Duration
1 month to 2 months
Language
English
Course Fee
HK$8000 per programme
Apply Now
Deadline on 02 Nov 2019 (Sat)
Enquiries
2867 8331
2858 4750
Now accepting application for November intake!

In the Big Data era, Machine Learning (ML), an important branch of Artificial Intelligence (AI), adopts scientific study of algorithms and statistical models to improve their 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 will cover popular techniques of ML and Predictive Analytics 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 would like to acquire the knowledge of Big Data and Machine Learning to assist their decision making. Also, learners who plan to acquire knowledge of Data Analytics and apply ML in their workplace are highly welcomed. To handle Big Data, the basics of programming language will be briefly delivered at the beginning and various Machine Learning Models will be illustrated with program code in a simple manner. No advanced statistical knowledge or programming skills is assumed.

"Think BIG DATA, Think HKU SPACE"

The Executive Certificate in Financial Decision Making: Big Data and Machine Learning 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.

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.

Teachers:

1) 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. 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.

3) 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.

Application Code 1755-EP128A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 2:00pm - 7:00pm
Duration
  • 30 hours per module
Venue
  • HK Island

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

November 2019 intake (M22164)

Lecture Date  Time
1 16 Nov 19 (Sat) 14:00 - 19:00
2 23 Nov 19 (Sat) 14:00 - 19:00
3 30 Nov 19 (Sat) 14:00 - 19:00
4 7 Dec 19 (Sat) 14:00 - 19:00
5 14 Dec 19 (Sat) 14:00 - 19:00
6 21 Dec 19 (Sat) 14:00 - 19:00

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

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

Application Fee

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

Course Fee
  • Course Fee : HK$8000 per programme (course fees are subject to change without prior notice)

Online Application Apply Now

Application Form Download Application Form

Enrolment Method

HKU SPACE provides 24-hour online application and payment service for students to make enrolment for most open admission courses (courses enrolled on first come, first served basis) and selected award-bearing programmes via the Internet.  Applicants may settle the payment by using either PPS, VISA or Mastercard online.

  1. Complete the online application form

    Applicant may click the icon Apply Now 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 be admitted 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. 

  2. Make Online Payment

    Pay the programme/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 including the “HKU SPACE Mastercard”.

To know more about online enrolment and payment, please refer to the user guide of Online Enrolment and Payment:

 

In Person / Mail

For first time enrolment

Applicants must provide all the required information on the application form and any additional information as required after the intial application assessment. Otherwise the School may not be able to process the admission/enrolment further.

  1. 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.  
  2. 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 course

In person or by post
  1. 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.
  2. 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.

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:

  1. To make an application online, you will need a computer with connection to the Internet and a web browser with JavaScript enabled. Internet Explorer 5.01 or above is recommended for the web browser.
  2. Applicants should not leave the online application idle for more than 10 minutes.  Otherwise, applicants must restart the application process.
  3. Only S-MILES and Early Bird Discount are supported in Online Applicants (Application).  To enjoy other types of discount, please visit one of our enrolment centres.
  4. 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.
  5. Applicants are reminded that they should only apply for the same programme/course once through counter or online application.
  6. For online enrolment, 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.
  7. Fees paid are not refundable except as statutorily provided or under very exceptional circumstances (e.g. course cancellation due to insufficient enrolment).
  8. 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.

Payment Method
1. Cash or EPS

Cash or EPS are accepted 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 write the programme title(s)  and the applicant’s name on the back of the cheque. You may either:

  • in person by submitting the payment, completed form(s), and required supporting documents to any of our enrolment centres; or
  • by mailing the above documents to any of our enrolment centres, specifying “Course Application” on the envelope. 
3. VISA/MasterCard

Course applicants, who are holders of HKU SPACE Mastercard, can enjoy a 10-month interest-free instalment period for courses of HK$2,000 and over. For enquiries, please contact our enrolment centres.

4. Online payment

Online payment for short courses (first come, first served) and selected award-bearing programmes is available using PPS, VISA or Mastercard. 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 online PPS will be reimbursed by a cheque; fees paid by credit card will be reimbursed to credit card account used for payment.
  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 teams for details.
  4. Fees and places on courses are not transferrable. 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.
  5. HKU SPACE will not be responsible for any loss of payment, receipt, or personal information sent by mail.
  6. For additional copies of receipts, 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. Such copies will normally be issued at the end of a course.