Programmes

Master of Science in Data Science

Programme	Master of Science in Data Science 理學碩士(數據科學)
Award Title	Master of Science in Data Science 理學碩士(數據科學)
Offering Academic Unit	Department of Data Science
Mode of Study	Combined mode

Normal Period of Study

- 1 year (full-time)
- 2 years (part-time/combined)

Maximum Period of Study

- 2.5 years (full-time)
- 5 years (part-time/combined mode)

Credit Units Required for Graduation

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

Programme Requirements

1. Core Courses (15 credit units)

Course Code	Course Title	Credit Units	Remarks
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	Pre-requisite(s): SDSC5001 Statistical Machine Learning I
SDSC6002	Research Projects for Data Science	3

2. Electives (15 credit units)

Course Code	Course Title	Credit Units	Remarks
CS5285	Information Security for eCommerce	3
CS5487	Machine Learning: Principles and Practice	3	Pre-requisite(s): CS3334 Data Structures AND (MA2176 Basic Calculus and Linear Algebra or MA2170 Linear Algebra & Multi-variable Calculus or MA2172 Applied Statistics for Sciences & Engineering)
CS6290	Privacy-enhancing Technologies	3	Pre-requisite(s): CS5285 Information Security for eCommerce
CS6493	Natural Language Processing	3	Pre-requisite(s): CS5286 Algorithms and Techniques for Web Searching or CS5487 Machine Learning: Principles and Practice or CS5489 Machine Learning: Algorithms and Applications or CS5491 Artificial Intelligence
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	Pre-requisite(s): SDSC5001 Statistical Machine Learning I
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	Precursor(s): Basic Machine Learning Knowledge
SDSC8011	Social Foundations of Data Science	3
SDSC8013	Statistical Methods for Categorical Data Analysis	3
SDSC8014	Online Learning and Optimization	3

Related Links

Department of Data Science