MSc Data Science
September 2024
Full Time
24 months
Suzhou
This programme leads to a University of Liverpool degree which is recognised by Ministry of Education, China.
Knowledge and skills
During the programme, students will study a range of modules in mathematics, statistics, information science and computer science to prepare for a career working across a number of industries. Students can gain data science knowledge used in IT, finance, biostatistics, bioinformatics, material science, actuarial science and others. The MSc Data Science programme will enable students to gain a strong understanding of statistics and computer science, and their applications.
Upon graduation, students will be able to:
- demonstrate proficiency in the use of statistical methods to solve practice problems related to data science;
- demonstrate advanced mathematical and analytical problem-solving skills;
- use software as an effective tool for data analysis, management and visualisation;
- evaluate and apply alternative approaches in the analysis of academic and industrial reports;
- present reasonable arguments and statements demonstrating specialised knowledge;
- communicate effectively with leaders and team members; and
- evaluate research papers and professional texts to produce independent synthesis of knowledge and information.
From finance and economy to biology and medicine, from manufacturing to infrastructure, data science plays a vital role in all aspects of the modern world. Our MSc Data Science will equip you with an advanced level of skills, knowledge, and experience to pave the way to your career prosperity.
Dr Long Bai
Programme director
Modules
*Programme modules listed are illustrative only and subject to change. XJTLU students are advised to log in to the e-Bridge Portal to view the effectuated module structure.
Semester 1
Data Mining and Big Data Analytics
Database Management
Mathematics And Statistics For Data Science
Semester 2
Statistical Learning With Applications In Python
Optimization Algorithms For Data Science
Advanced Statistical Learning Theory
Semester 3
Dissertation
Semester 4
Dissertation
Semester 1
Advanced Methods in Biostatistics
Research Methods in Bioinformatics
Fundamentals of Machine Learning
Linear Statistical Models
Computational Methods in Finance I
Semester 2
Statistical Computing Using SAS
Information Visualization
Time Series Analysis
Computational Methods in Finance II
Neural Networks And Deep Learning
Careers
Graduates of this programme will be prepared for pursuing advanced degrees (e.g. PhD degree) or for direct entry into the job market in various sections and positions:
- manufacturing
- new materials
- information technology
- biology and medicine
- finance
- database management
- bioinformation
- artificial intelligence and machine learning
- logistics and e-commerce
- public services
Overview
The MSc Data Science programme will equip students with the analytical tools to design sophisticated technical solutions for data-driven problems using modern computational methods and with an emphasis on rigorous statistical thinking.
This programme lasts for two years, including 40 credit teaching modules and a 40-credit dissertation. The professional curriculum is suitable for graduates in various fields such as mathematics, engineering, computer science and social science. Teaching modules include compulsory courses for theoretical training of core disciplines including mathematics, statistics and computer science, as well as optional courses suitable for different application scenarios (such as artificial intelligence, biological information, and finance).
Students can customise their training through various combinations of compulsory and optional modules to support different students’ interests and career planning.
Moreover, as one of the joint master programmes with Jiangsu Industrial Technology Research Institute (JITRI), the student dissertation in this programme can be jointly guided by the industry experts from JITRI and academic supervisors from XJTLU. This programme provides a platform for knowledge and information exchange between university and enterprise and promotes cooperation between the two sides in key fields such as engineering mathematics, applied statistics, bioinformatics, and material science.