Master of Science in Data Science (MSDS)

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Programme Aims

The programme aims to produce data-analytic graduates to meet the growing demand for high-level data science skills and to prepare graduates to apply data science techniques to knowledge discovery and dissemination in organisational decision-making. It is also intended to help data analytic professionals upgrade their technical management and development skills, and to provide a solid path for students from related quantitative fields to rapidly transition to data science careers.

Programme Intended Learning Outcomes (PILOs):

Upon successful completion of this Programme, students should be able to:

1. Apply knowledge of science and engineering appropriate to the data science discipline
2. Understand theoretical foundation of contemporary techniques and apply them for managing, mining and analysing data across multiple disciplines
3. Comprehend computational tools and use data-driven thinking to discover new knowledge and to solve real-world problems with complex structures
4. Recognise the need for and engage in continuous learning about emerging and innovative data science techniques and ideas
5. Communicate ideas and findings in written, oral and visual forms and work in a diverse team environment

Course List

Core Electives (15 credit units)

Course Code	Course Title	Credit Units
SDSC5001	Statistical Machine Learning I	3
SDSC5002	Exploratory Data Analysis and Visualization	3
SDSC5003	Storing and Retrieving Data	3
SDSC6001	Statistical Machine Learning II	3
SDSC6002	Research Projects for Data Science	3

 

Electives (15 credit units)

Course Code	Course Title	Credit Units
CS5285	Information Security for eCommerce	3
CS5487	Machine Learning: Principles and Practice	3
CS6290	Privacy-enhancing Technologies	3
CS6493	Natural Language Processing	3
SDSC6003	Bayesian Data Analysis	3
SDSC6004	Data Analytics for Smart Cities	3
SDSC6006	Dissertation	6
SDSC6007	Dynamic Programming and Reinforcement Learning	3
SDSC6008	Experimental Design and Regression	3
SDSC6009	Machine Learning at Scale	3
SDSC6011	Optimization for Data Science	3
SDSC6012	Time Series and Recurrent Neural Networks	3
SDSC6013	Topics in Financial Engineering and Technology	3
SDSC6014	Networked Life and Data Science	3
SDSC6015	Stochastic Optimization for Machine Learning	3
SDSC6016	Predictive Analytics and Financial Applications	3
SDSC8007	Deep Learning	3
SDSC8008	Data-driven Operations Research	3
SDSC8009	Data Mining and Knowledge Discovery	3
SDSC8011	Social Foundations of Data Science	3
SDSC8013	Statistical Methods for Categorical Data Analysis	3
SDSC8014	Online Learning and Optimization	3

The full MSc degree award requires 30 credit units, with the completion of taught courses only; or taught courses plus the dissertation project.

Remarks: Programme electives will be offered subject to availability of resources and sufficient enrolment.

Student Handbook

• 2024/25
• 2023/24
• 2022/23
• 2021/22
• 2020/21

(Note: The handbooks are updated as at the beginning of the corresponding academic years.)

Admission Requirements

Applicant must be a degree holder in Engineering, Science or other relevant disciplines, or its equivalent

Non-local candidates from an institution where the medium of instruction is NOT English should fulfil one of the following English proficiency requirements.

• a score of 79 (Internet-based test) in the Test of English as a Foreign Language (TOEFL)^@#; or
• an overall band score of 6.5 in International English Language Testing System (IELTS)^@; or
• a minimum score of 450 in band 6 in the Chinese mainland’s College English Test (CET6); or
• other equivalent qualifications
  
  ^@ TOEFL and IELTS scores are considered valid for two years. Applicants are required to provide their English test results obtained within the two years preceding the commencement of the University's application period.
  
  ^# Applicants are required to arrange for the Educational Testing Service (ETS) to send their TOEFL results directly to the University. The TOEFL institution code for CityU is 3401.

Tuition Fees

HK$10,400 per credit (for local and non-local students admitted in 2025/26)
Credit Units Required for Graduation: 30

Duration of study:
Normal Study Period	Maximum Study Period
1 year (Full-time)	2.5 years (Full-time)
2 years (Part-time/Combined mode)	5 years (Part-time/Combined mode)

Career Prospects

Our MSDS programme offers comprehensive and rigorous training for students seeking a profession in data science. Our graduates have embarked on exciting and highly rewarding careers such as data scientists, data analysts, data engineers, AI engineers, professional consultants, managers, and other data expert positions. These careers, which have excellent prospects for growth and high compensation, are in high demand in industries such as finance and banking, technology, real estate, insurance, education, e-commerce, retail and marketing, and transportation and logistics.

Our graduates from the past few years have found employment in prestigious companies that include members of the Big Four accounting firm, tech giants, retail giants, and international banks. Moreover, they are shouldering critical roles that involve the use of data science to aid highly impactful tasks such as strategic business and operations decision-making, and innovative product and process development. Their careers spread across Hong Kong, the USA, and Mainland China (e.g. Beijing, Ningbo, Chongqing, Shanghai, and Shenzhen, etc.) Around 60% of our surveyed graduates receive a monthly salary of over HKD$30,000. Some of our graduates are also furthering their studies in PhD programmes at world-renowned universities.

Contact Us

For general enquiries, please contact the Department of Data Science (DS) at ds.go@cityu.edu.hk
For application enquiries, please contact the School of Graduate Studies (SGS) at tpadmit@cityu.edu.hk
For student visa matters, please contact the Global Engagement Office (GEO) at geovisa@cityu.edu.hk