Computing and Data Science Data Science
This programme is designed to help students develop basic skills in big data analytics. It provides students with the opportunity to gain in-depth knowledge and critical understanding of a range of issues and concepts in Big Data Analytics.
"Big data" is a term for the collection of large and complex data sets and the analysis of these data sets for relationships. Big data is characterized by 5 Vs: volume, variety, velocity, veracity and value. The quantity of data in these sets is so large that prevents traditional methods of analysis from being effective. Rather than focusing on precise relationships between individual pieces of data, big data uses various algorithms and techniques to infer general trends over the entire set. Good management of big data can lead to great insights; can help to figure out the root cause of problems and failures as well as deceitful behaviour that affects the revenues of a business. Therefore, big data analytics today becomes a key basis of competition and it will drive new waves of productivity, growth and innovation.
Intended Learning Outcomes (ILOs) of the Programme
On completion of the programme, students should be able to
- Apply big data analytics approach to solve business problems.
- Apply big data concept in marketing and retailing.
- Apply security solution in big data environment.
- Explore the potential use of Big Data tools and techniques in a business environment and fit these techniques within an organisation’s information system strategy
- Apply the knowledge of business intelligence and skills of mathematical modeling to synthesize, manage and evaluate information.
- Identify problems using a graph analytics approach and choose appropriate techniques to solve the problems.
News about Big Data Analytics Industry in Hong Kong:
Core Modules Title
1. Introduction to Big Data Applications and Analytics
2. Search Engine Optimisation and Web Analytics
3. Big Data Privacy and Information Security
4. Big Data and Information Systems
5. Big Data and Marketing Analytics
6. Big Data and Business Intelligence
7. Big Data and Mathematical Modeling
8. Graph Analytics for Big Data
Students will be assessed by continuous coursework, including assignments, projects, mid-term tests, and final examinations.
Modules & Class Details
IMAT4046 Introduction to Big Data Applications and Analytics
This module is designed to help students develop an appreciation of the Big Data analytics approach to problem formulation and solution. The aim is to build up students’ ability to understand the importance of the applications of Big Data analytics in their daily life, particularly in commercial areas. Emphasis will be placed on both application modeling and solution methodology.
IMAT4047 Search Engine Optimisation and Web Analytics
This module is designed to teach marketing strategies and tactics on the Internet with a combination of using search engine optimization (SEO) and web analytics. Students will learn how to reach target customers, increase social influence and improve search engine positioning. Key topics to be discussed include optimising searchable websites, link building, web metrics measurements and analytics data.
IMAT4048 Big Data Privacy and Information Security
Students will explore information security risks and different approaches that organizations can use to mitigate these risks and implement security plans and processes.
IMAT4049 Big Data and Information Systems
This module is designed to provide students with skills and techniques to manage Big Data information systems and examine the benefits of managing Big Data.
IMAT4050 Big Data and Marketing Analytics
This module builds on various analytical models typically used in marketing nowadays. It aims to develop student’s abilities to use big data to drive decision in a marketing context. The emphasis is not only on theory and concept but also on enhancing their learning experience in applying and interpreting a range of data sources to generate insight for developing marketing solutions.
IMAT4051 Big Data and Business Intelligence
This module is designed to provide students with a foundation of the conceptual, theoretical and practical knowledge in the fields of Big Data and Business Intelligence required for various business purposes. Students will be trained to synthesize, manage and evaluate the information together with the use of information technology to convey the results of the analysis appropriately for business decision-making.
IMAT4052 Big Data and Mathematical Modeling
This module covers the key characteristics and the process of Big Data analysis, including Big Data preparation and transformation. It focuses on how Mathematics and Statistics underpin Big Data analysis. Students will develop the ability to analyse and summarise the main characteristics of Big Data sets by using the Modeling Process and Mathematical Modeling.
IMAT4053 Graph Analytics for Big Data
This module is designed to introduce the fundamentals of graph analytics. Students will develop the ability to formulate real-world problems using the graph analytics approach and choose appropriate techniques to solve real-world problems.
Fee & Entry Requirements
- HK$52,000 (to be paid in 2 instalments)
- have gained in the HKDSE Examination Level 2 in 5 subjects, including English Language; OR
- have gained in the HKALE Grade E in 1 AL or 2AS subjects, and HKCEE Level 2 in English Language* or equivalent
* With effect from 2007, HKU SPACE recognizes Grade E previously awarded for English Language (Syllabus B) (Grade C in the case of English Language (Syllabus A)) at HKCEE as an acceptable alternative to Level 2 in this subject at HKCEE.
- The CEF Institution Code of HKU SPACE is 100
|Advanced Diploma in Big Data Analytics and Applications
|COURSE CODE 34F104400
|Introduction to Big Data Applications and Analytics (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z104411
|Search Engine Optimisation and Web Analytics (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z10442A
|Big Data Privacy and Information Security (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z104438
|Big Data and Information Systems (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z104446
|Big Data and Marketing Analytics (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z104454
|Big Data and Business Intelligence (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z104462
|Big Data and Mathematical Modeling (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z104470
|Graph Analytics for Big Data (Module from Advanced Diploma in Big Data Analytics and Applications)
|COURSE CODE 34Z104489
|Continuing Education Fund
This course has been included in the list of reimbursable courses under the Continuing Education Fund.
|Continuing Education Fund Reimbursable Course (selected modules only)
Some modules of this course have been included in the list of reimbursable courses under the Continuing Education Fund.
Advanced Diploma in Big Data Analytics and Applications
Application Form Download Application FormEnrolment Method
- All applicants are required to complete one Application Form (SF26) and, either submit them in person or post them to Ms Katrina Knip, College of Life Sciences and Technology, HKU SPACE, at any of the HKU SPACE Enrolment Centres.
- All applications, either by post or in person must be accompanied by:
- Certified photostat copies of full educational certificates and transcripts.
Note: When submitting your application in person at any of the HKU SPACE enrolment centers, please bring along the originals of your educational certificates / transcripts for certification. If you are posting your application, please arrange to bring the originals of the relevant documents for certification at any of HKU SPACE Enrolment Centres before the commencement of classes.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.
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.
- 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.