Master of Science in Data Science (MSDS)

理學碩士(數據科學)


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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 analyzing 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. Recognize 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 Panel Data 3
SDSC6013 Topics in Financial Engineering and Technology 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


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 medium of instruction is not English should fulfill one of the following English proficiency requirements.

  • a TOEFL score of 550 (paper-based test) or 59 (revised paper-delivered test) or 79 (Internet-based test) on 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

 

Tuition Fees

HK$8,450 per credit (for local and non-local students admitted in 2019/20)

Credit Units Required for Graduation: 30

Duration of study:
Normal Period Maximum Period
- 1 year (full-time) - 2.5 years (full-time)
- 2 years (part-time/combined) - 5 years (part-time/combined mode)

 

Career Prospects

A strong demand of the data scientists and analysts has been recently observed in the worldwide job market. This programme aims at producing analytic and business-aware graduates to meet the growing demand by equipping them with big data analytics skills and nurturing their capability in applying data science techniques to address emerging complicated real-life problems. Upon successful completion of this programme, the student should be able to:

  1. Apply data processing skills to handle data of various formats and sizes.
  2. Conduct comprehensive data analytics with integrating techniques from various disciplines for knowledge discovery and dissemination in organizational decision-making.
  3. Utilize a variety of data visualization techniques to interpret data analytics results.
  4. Demonstrate strong quantitative capabilities as well as communication skills.
  5. Develop descriptive, prescriptive and predictive analytics solutions to tackle emerging challenges in contemporary problems.

 

Contact Us

Programme Leader

Prof. WANG Junhui

 

For application enquiries, please contact School of Graduate Studies (SGS) at tpadmit@cityu.edu.hk
For visa matters, please contact Global Services Office (GSO) at gsoins@cityu.edu.hk

 


Last modified on 14 August, 2019