ASDK20002U Elementary Social Data Science
Mandatory course on MSc programme in Social Data Science at University of Copenhagen. The course is only open for students enrolled in the MSc programme in Social Data Science.
This course provides students with a general introduction to the research process in social data science. The course introduces central concepts and research methods in relation to the planning and conduction of research in the field of social data science. The course is structured in three constitutive blocks.
The first block provides an introduction to the different forms of data (e.g., data from quantitative and qualitative research, “big data”) and prominent research designs.
The second block introduces different data collection methods (e.g., found data, surveys, and experiments).
The third block introduces principles and methods on how to establish high data quality (e.g., high validity and reliability) as well as how to treat data from various sources (e.g., transforming and structuring data).
In all, the course introduces the students to basic techniques, methods and principles of social data science research to prepare them for and complement the advanced computational techniques, statistical methods and social science theories taught in subsequent courses.
- Explain the principles of empirical social science informing both quantitative and qualitative research.
- Account for a broad variety of data collection methods used in social data science, as well as their strengths and weaknesses.
- Account for basic methods how to process and treat data for further analyses.
- Explain common criteria for high-quality, replicable social science research.
- Develop social data science research designs.
- Plan data collections of primary data to answer research questions using survey and experimental methods.
- Plan data collections of secondary data to answer research questions from online sources using web scraping, online archives, and APIs.
- Evaluate data quality and prepare data for further statistical analyses.
- Evaluate and critically reflect on published social data science research by applying the highest international standards.
- Identify opportunities to use digital data sources.
- Plan and conduct high-quality social data science research projects, encompassing the research design, data collection, and data preparation stages
Book chapters and scientific articles related to the course content. Students have to prepare lectures/exercises by reading about 50-100 pages per week. Readings will be provided by the teachers.
- Project work
Students will be registered by the student administration.
- 7,5 ECTS
- Type of assessment
- PortfolioExam form Portfolio exam written in groups. The portfolio consists of revisions to the earlier assignments that have been handed in and must be submitted by the end of the course. The final grade results from the combined assessment of the three assignments.
- Exam registration requirements
During the course, students must in groups submit a set of compulsory assignments, each corresponding to one of the three blocks
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
The second and third examination attempts are conducted in the same manner as the ordinary examination.
Criteria for exam assesment
The exam will be assessed on the basis of the learning outcome (knowledge, skills and competencies) for the course.
- Course code
- 7,5 ECTS
- Full Degree Master
- 1 block
- Block 2
- Course capacity
- 70 students.
- Study board
- Social Data Science
- Social Data Science
- Faculty of Social Sciences
- Friedolin Merhout (8-686f67746a71777642757165306d7730666d)
- Hjalmar Alexander Bang Carlsen (2-76714e817d713c79833c7279)
Friedolin Merhout, Hjalmar Alexander Bang Carlsen, Helene Willadsen