MSc in Computational Social Science
The MSc programme in Computational Social Science aims to strengthen students’ ability to conduct interdisciplinary and innovative research by combining computational methods with one or several social science and humanities disciplines, including but not limited to political science, international relations, Chinese studies, linguistics, psychology, public policy, urban studies, sustainable development, public administration, sociology, history, literature, anthropology, and archaeology. It will train students in social science to apply computational techniques and solve problems in social science and humanities. Applicants should have an educational background in computer science or at least one social science or humanities discipline.
| Course Groupings | Course Units | Minimum Grade | Units Required for Graduation |
| I. Required Courses (9 units) | |||
| CSS5010 Social Science Research Methods | 3 | C | 9 |
| CSS5020 Statistics for Social Science | 3 | C | |
| CSS5220 Computational Thinking with Social Science Applications | 3 | C | |
| II. Elective Courses - Group I (9 units) | |||
| CSS5110 Advanced Topics in Sociology | 3 | C | 9 (three courses from Group I) |
| CSS5120 Computational Linguistics | 3 | C | |
| CSS5130 Advanced Topics in Economics | 3 | C | |
| GLB5330 Comparative Politics | 3 | C | |
| PSY5150 Advanced Topics in Educational Psychology | 3 | C | |
| II. Elective Courses - Group II (9 units) | |||
| CSS5210 Advanced Topics in Social Statistics | 3 | C | 9 (three courses from Group II ) |
| CSS5230 Introduction to Machine Learning for Social Science | 3 | C | |
| CSS5240 Data Visualization for Social Science | 3 | C | |
| CSS5250 Psychological Testing | 3 | C | |
| CSS6230 Introduction to Social Computing | 3 | C | |
| Total Units: | 27 | ||
| Required: | 9 | ||
| Elective: | 18 | ||
| Graduation Requirement | |||
| All students are required to meet all the programme requirements, pass all the assessments and obtain a cumulative overall GPA of at least 2.0 in order to graduate. | |||
*Applicable to students admitted in AY2024-2025
| Course Groupings | Course Units | Minimum Grade | Units |
| I. Required Courses | |||
| CSS5010 Social Science Research Methods | 3 | C | 3 |
| CSS5020 Statistics for Social Science | 3 | C | 3 |
| CSS5220 Computational Thinking with Social Science Applications | 3 | C | 3 |
| CSS5030 Independent Research Project | 6 | C | 6 |
| II. Elective Courses - Group I (at least 9 units) |
Students shall take: and 3 courses (9 units) from Groups II; or 2) 3 courses (9 units) from Group I, and 4 courses (12 units) from Groups II. |
||
| CSS5110 Advanced Topics in Sociology | 3 | C | |
| CSS5120 Computational Linguistics | 3 | C | |
| CSS5130 Advanced Topics in Economics | 3 | C | |
| CSS5140 Advanced Topics in Empirical Research on Development | 3 | C | |
| CSS5150 Advanced Topics in Regional and Urban Development | 3 | C | |
| CSS5160 Advanced Topics in International Politics | 3 | C | |
| CSS5170 Advanced Topics in Globalization | 3 | C | |
| CSS5180 Advanced Topics in Demographics | 3 | C | |
| GLB5330 Comparative Politics | 3 | C | |
| GLB5340 Comparative Public Opinion | 3 | C | |
| PSY5150 Advanced Topics in Educational Psychology | 3 | C | |
| II. Elective Courses - Group II (at least 9 units) | |||
| CSS5210 Advanced Topics in Social Statistics | 3 | C | |
| CSS5211 Advanced Statistical Modelling in Social Science | 3 | C | |
| CSS5230 Introduction to Machine Learning for Social Science | 3 | C | |
| CSS5240 Data Visualization for Social Science | 3 | C | |
| CSS5250 Psychological Testing | 3 | C | |
| CSS5260 Experimental Methods in Social Science | 3 | C | |
| CSS5270 Causal Inference | 3 | C | |
| CSS5280 Modeling and Analysis of Complex System | 3 | C | |
| CSS5290 Applied Time Series Analysis | 3 | C | |
| CSS6230 Introduction to Social Computing | 3 | C | |
| Total Units: | 36 | ||
| Required: | 15 | ||
| Elective: | 21 | ||
| Graduation Requirement | |||
| All students are required to meet all the programme requirements, pass all the assessments and obtain a cumulative overall GPA of at least 2.0 in order to graduate. | |||
*Applicable to students admitted in AY2025-2026
Course and Unit Exemption
Students may be granted exemption from courses if they have taken similar or equivalent postgraduate courses with satisfactory performance (“B” grade or above). Supporting documents such as academic transcripts, course syllabus should be provided for evaluation. Exemption for a maximum of six units may be granted. In case of course exemption only, a student is required to take an elective course of equal number of units to replace the exempted course. Applications will be assessed individually by the relevant faculty committee of the programme and will be subject to the University’s final approval.
