Accounting & Finance FinTech and Financial Analytics

Executive Certificate in Text Analytics and NLP with Financial Technology
行政人員證書《金融科技：文字分析與自然語言處理》

Course Code: EP163A
Application Code: 2260-EP163A

Study mode: Part-time

Start Date: 03 Dec 2024 (Tue)

Next intake(s): Apr 2025

Duration: 2 months to 3 months

Language: English

Course Fee: Course Fee: $9000 per programme (* course fees are subject to change without prior notice)

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Deadline on 19 Nov 2024 (Tue)

Enquiries: 2867 8331 / 2867 8424

2861 0278

finedec@hkuspace.hku.hk

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Today and Upcoming Events

27
Nov 2024
(Wed)

How can we automate Invoice Matching Process and design a Customer Debt Aging Dashboard System? (27 Nov 2024)

Having received customer invoice payments, AR team members would need to match up the received payments with the corresponding customer orders (or invoices), which would be partially matched, fully matched or even overpaid. Collectively, the team members would need to prepare a customer aging analysis report for management based on the outstanding or overdue invoice amounts for debt analytics. The pain point is that all these are done manually with Excel spreadsheets and it would take much time to reconcile all of worksheets. A perfect solution is to get all these routine tasks automated with Excel automation (no-coding approach) with a dashboard system. If you are interested in knowing a bit about the design, please don’t hesitate to register for the coming lunch talk with the following link. Mr Danny Chan, the speaker, lecturer and data consultant, will present to you the solution with a live demo. Language: Cantonese (Supplemented with English)

02
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(Mon)

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18
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Online Executive Certificate / Diploma Information Seminar - Big Data & FinTech Series (18 Dec 2024)

The recent advances in Big Data and AI have major impact on the investment and trading community. Now different types of alternative data from news, social sentiment to satellite images can be used to construct and manage investment portfolios. Moreover, Machine Learning is applied to stock price predictions while Reinforcement Learning (Alpha-Go) technique is employed into trading strategies discovery. This programme is suitable for degree holders and Executives who wish to enhance the...

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Online Executive Certificate / Diploma Information Seminar - Big Data & FinTech Series (8 Jan 2025)

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Programme Overview

Highlights

The programme aims to cover the latest applications of financial technology by using text analytics and natural language processing (NLP). It provides students with a foundation in collecting, processing, managing and analyzing textual data using computational tools. It discusses the use of NLP, text analytics and machine intelligence. The programme also offers summarization and visualization of results obtained from text analytics and NLP for applications in finance.

Programme Details

On completion of the programme, students should be able to
1. identify the main elements of text analytics and natural language processing;
2. examine techniques of text analytics and natural language processing;
3. apply machine intelligence to analyze and visualize textual data using computation tools;
4. discuss the latest applications of text analytics and natural language processing in financial technology.

Application Code	2260-EP163A	Apply Online Now
Apply Online Now

Days / Time

Tue - Wed, 7:00pm - 10:00pm

Modules & Class Details

Modules

Course Content:

Introduction to Text Analytics and Natural Language Processing (NLP)

Technological overview of text analytics and NLP in finance and investment: text mining, web mining, data mining, information retrieval and NLP
Principles of text analytics
- Key elements of text analytics
- Textual data wrangling: collecting, importing, organizing and cleaning textual data
- Text mining and text analytics: web scraping, textual corpora, text processing, tokenization, stemming and stop word removal
Essential of natural language processing (NLP)
- Core elements of NLP
- Lexical parsing, syntax parsing, semantic parsing
- NLP and computational linguistics

Machine Intelligence, Text Analytics, ChatGPT, and NLP in Financial Technology

Development of machine intelligence in financial technology: AI, Machine Learning, and Deep Learning
Introduction to computation tools for text analytics and NLP: R, Python, Tensorflow, Pytorch
Basic algorithms from deep learning: linear regressions, SVM
Introduction to Neural Networks for Text Analytics and NLP: Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Generative Adversarial Networks (GAN)
Techniques for text analytics and NLP: bag-of-words, word weighting schemes, document classification, document clustering, sentiment analysis, and model building
Introduction of ChatGPT and its difference with traditional text analytics
Generative model for large language model (LLM)

Text Analytics and NLP in Financial Decision Making

Real-world financial information and textual datasets
- Financial texts from corporate disclosures, financial reports, professional periodicals, aggregated news, message boards and social media
- Summarization of textual data and information
- Display and visualization of results: word cloud
- Financial intelligence from analyzed textual data
Applications and Issues of text analytics and NLP for financial decision making
- Statistical and cultural bias in datasets
- Financial slang and NLP for sentiment analysis
- Analysis of financial textual data
- NLP and deep learning for predicting stock price movements
- Semantic model building for financial forecasting
- Credit risk analysis using text analytics
- Implications and limitations of text analytics and NLP in making finance and investment decision
- Improving financial services with NLP

Assessment method: Two assignments exercise + group presentation

The Executive Certificate will be conferred to candidates who have attained PASS grade and achieved at least 70% attendance of the programme.

Teacher

(1) Ms Rowena Lai
Ms. Lai has extensive experience in business and data analytics in different business sectors. She is currently working in a well-known international bank and leading various data analytics projects. She graduated from the Chinese University of Hong Kong with a Bachelor of Science degree (major in Mathematics and Minor in Economics), and obtained a Master of Science in International Shipping and Transport Logistics as well as a Master of Science degree in Global Supply Chain Management from the Hong Kong Polytechnic University. Ms. Lai is currently a Certified Analytics Professional (CAP) from INFORMS. With her practical experience in data analytics and professional knowledge in financial technology, she teaches "Big Data and FinTech" module under Postgraduate Diploma in Investment Management and Financial Intelligence, Executive Certificate in Big Data and Data Analytics as well as Executive Certificate in Text Analytics and NLP with Financial Technology.

(2) Mr Clive Yip
Mr. Yip is a practitioner in Data Analytics. He has 10 years of experience in both Big 4 consulting firms and multinational companies. He is currently working as a Senior Data Analytics Consultant in a leading insurance company, using Python, SQL and other Big Data technologies to analyse and monitor any non-compliance or fraudulent activities. He has a Master’s degree in Information Technology from HKUST and a Bachelor’s degree from the University of Southern California. Before entering the data analytics field, he worked as a financial auditor in Ernst and Young and is a Certified Public Accountant (CPA) in Hong Kong and Canada.

(3) Professor Daniel Chan

Professor Chan is a veteran IT management consultant having held CTO, ED, INED, GM and Director positions in MNC’s with 20+ years of experience. Currently, he runs a couple of AI companies in the space of data annotation and pose detection on top of his advisory role to other AI companies working in different spaces. His largest text analytics project involves 60M text documents which calls for the latest technologies available in Natural Language Processing (NLP). He is also the course leader for “Machine Learning for Financial Data” in an MSc FinTech programme in a university in Hong Kong.

(4) Dr Cheung King Hong

Dr Cheung has a deep interest in applying data technologies in different functions and domains to enhance the quality of human life and environment. He has deep understanding and insight about the use and integration of technologies in business through his personal network, past experiences on consultancy as well as rich experiences in lecturing a wide range of subjects during his career as a teaching staff member in one of the universities in Hong Kong. He has leaded and guided master students to apply Data Sciences techniques, including Artificial Intelligence, Machine Learning, Data Analytics/Mining in research on medical operations and metal fracture detection in Hong Kong.

Class Details

Timetable

Dec 2024 intake

Lecture	Date	Time
1	3 Dec 24 (Tue)	19:00-22:00
2	4 Dec 24 (Wed)	19:00-22:00
3	10 Dec 24 (Tue)	19:00-22:00
4	11 Dec 24 (Wed)	19:00-22:00
5	17 Dec 24 (Tue)	19:00-22:00
6	18 Dec 24 (Wed)	19:00-22:00
7	7 Jan 25 (Tue)	19:00-22:00
8	8 Jan 25 (Wed)	19:00-22:00
9	14 Jan 25 (Tue)	19:00-22:00
10	15 Jan 25 (Wed)	19:00-22:00

Remarks :
-Tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers.
-For your safety and health, please note that the School may substitute face-to-face classes with online teaching if necessary.

Articulation Pathways

Progression Path

Fee & Entry Requirements

Fee

Application Fee

HK$150 (Non-refundable)

Course Fee

Course Fee: $9000 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 work experience.

Applicants with qualifications in quantitative areas (e.g., mathematics, engineering, statistics, computer science, information technology, economics, finance) are preferred. Those with other qualifications and substantial senior level work experience will be considered on individual merit.

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Online Application Apply Now

Application Form Download Application Form

Enrolment Method

Online 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:

- Short Course

- Award-bearing Programme

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.

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 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 personal information and 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 (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, new and continuing students of award-bearing programmes with available online service, they may also pay their course fees by Online WeChat Pay, Online Alipay or 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 Enrolment Centres and avoid making cheque payment under this circumstance.
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, FPS or PPS by Internet will be reimbursed by a cheque, and fees paid by credit card will be reimbursed to the credit card account used for payment.
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.
HKU SPACE will not be responsible for any loss of payment, receipt, or personal information 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.