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Master of Science in Data Science and Analytics
數據科學及分析理學碩士學位0
Programme Aims
In today's era of big data, large data sets are generated every day in various areas of society and industry, such as the Internet, social networking and finance. It is a challenging task to analyse and extract information from this unprecedentedly large volume of data. To create value from such data, one must combine techniques from mathematics, statistics and computer science. This programme nurtures graduates with expertise that cuts across the core disciplines of mathematics, statistics and computer science. It develops students’ analytical and critical thinking, as well as their problem-solving skills. This enables graduates to pursue careers as data analysts in various industries, such as finance and information technology.
Characteristics
Data Science and Analytics involves the use of mathematical, statistical and computing techniques to extract useful information from large-scale data and make decisions accordingly. Statistics, optimisation methods and computer science are widely acknowledged to form the three pillars of modern data science. This programme is designed to provide a balanced treatment of these three pillars, with the aim of cultivating future data analysts.
Graduates who have highly developed mathematical, statistical and computing skills are thus in great demand globally, in both industry and research.
- A Bachelor's degree with Honours in mathematics, statistics, computer science, IT, engineering, science, or equivalent. Applicants with a Bachelor’s degree in another discipline and an adequate background in mathematics or IT will also be considered.
Core Areas of Study
6 Compulsory Subjects (18 credits)
- Advanced High Dimensional Data Analysis
- Big Data Computing
- Data Structures and Database Systems
- Deep Learning
- Optimization Methods
- Principles of Data Science
Elective Subjects (Each subject carries 3 credits)
- Advanced Data Analytics
- Advanced Operations Research Methods
- Advanced Topics in High Frequency Trading
- Applied Linear Models
- Artificial Intelligence Concepts
- Decision Analysis
- Dissertation
- Forecasting and Applied Time Series Analysis
- Graphs and Networks
- Investment Science
- Loss Models and Risk Analysis
- Mathematical Modeling for Science and Technology
- Multi‐criteria Optimization
- Operations Research Methods
- Optimal Control with Management Science Applications
- Probability and Stochastic Models
- Scientific Computing
- Simulation and Risk Analysis
- Statistical Data Mining
- Statistical Inference
