Big Data Analytics in Finance

Programme Code: COMP0003

Faculty of: Engineering, Department of Computer Science

Level-of-study: S4-S6

Area of Interest: Science

Programme Period: July 11, 2022 to July 22, 2022

Programme Schedule: Monday to Friday (10:00 - 12:00)

Programme Fee: HKD 5,800

Teaching Mode: Online

Discount(s) on Programme Fee:
20% - Academy Summer Scholarship / HKUSI Alumni Discount
10% - Referral Discount / HKU Members Discount
5% - Early Bird Discount (Deadline: February 28, 2022) / Private Discount
* All discounts are NOT in conjunction with any other discounts.
For further details, please visit here.

Deadline for Application:
June 10, 2022

Online ApplicationAPPLY NOW

This programme has been changed from face-to-face to online delivery mode.

Course Description:

This course studies how to use advanced Machine Learning technologies for predicting financial data. In the first part of this course, we explain what Machine Learning is and list some of its major applications. We also describe the key components in most machine learning tools and the types of learning these tools can handle. In the second part of this course, we illustrate how to apply some powerful machine learning tools to predict stock prices. Then, we show how to apply Natural Language Processing on twitter tweets to analyze automatically the sentiment of stock investors in order to improve our predictions.

Course Instructor(s):

Dr. H.F. Ting, Associate Professor of Department of Computer Science

Course Outline:



• Students of non-credit bearing programmes will receive a certificate upon programme completion. Students of credit-bearing programmes can apply a transcript stated with HKU credits, please contact your home institution about the issue related to HKU credits recognition.

• You understand that you must be available for the entire duration of the programme, from the start to the end of the programme. 20% absence is allowed for emergencies or sick leave with legitimate reason and official letterhead proof for the absence. Students have the responsibility to contact the programme coordinator and/or teacher concerned about this ahead of time. No exceptions will be made, unless prior approval has been given. You are expected to be punctual for all classes and tutorials. Unexcused cases of lateness may count as absence. Students who fail to achieve an 80% of attendance or absence without legitimate reason and official letterhead proof for the absence will not be awarded the HKU Credits or the Certificate of Completion.