DSC2005 - Introduction to Computational Social Science | ||||||||||||
| ||||||||||||
| * The offering term is subject to change without prior notice | ||||||||||||
Course Aims | ||||||||||||
Data science centers around data, originated by human or non-human. This course provides students with an extensive exposure to the elements of computational social science that concerns exclusively with human-generated data. Topics include opportunities and challenges for social science research in the digital age, descriptive/predictive vs. explanatory research, found data versus made data, research design, causal inference, sampling of social units, online experiment, behavioral analytics, text mining, and social research ethics. | ||||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||||
Continuous Assessment: 50% | ||||||||||||
Examination: 50% | ||||||||||||
Examination Duration: 2 hours | ||||||||||||
Min. Continuous Assessment Passing Requirement: 30% | ||||||||||||
Min. Examination Passing Requirement: 30% | ||||||||||||
Note: To pass the course, apart from obtaining a minimum of 40% in the overall mark, a student must also obtain a minimum mark of 30% in both continuous assessment and examination components. | ||||||||||||
Detailed Course Information | ||||||||||||
| DSC2005.pdf | ||||||||||||