Social Data Analysis
Course content
This course introduces theories, concepts, and methods for the social scientific study of behaviour, social networks and cultural ideas. Through a combination of lectures, seminars and exercises, the course shows how classic social science problems can be investigated by using data science approaches, and how the study of large-scale digital social data can benefit from social science approaches. As such, the course provides students with knowledge about central methodologies and theories of social data science research, and with the capacity to operationalize these.
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.
At the end of the course, students are able to:
Knowledge:
- Account for key social science theories of behaviour, networks and ideas.
- Demonstrate understanding of how social data science approaches can improve social science theories and vice versa.
Skills:
- Assess the relevance of computational social data science approaches to investigate social data science problems.
- Identify and operationalize relevant theoretical concepts and constructs.
Competences:
- Ability to perform the conceptual work necessary for social data science projects.
- Apply best practices in operationalizing relevant social data science theories pertaining to behaviour, networks and ideas.
A combination of lectures introducing central theories and methods of behaviour, networks and ideas, with seminars, including student presentations and group discussions.
Book chapters and scientific articles related to the course content. The students may be asked to purchase one or two books for general background.
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Home assignment
- Type of assessment details
- Students are placed in assigned groups of 3 to 4 at the
beginning of the course. Students have to submit 4 group
assignments as prerequisites to register for the exam (3 out of 4
must be approved). These have to be submitted in the assigned
groups. For the final exam, students can choose to write alone, or
in groups up to 4 of their own choosing.
Lenght of home assignment: 15 pages - Examination prerequisites
-
3 out of the 4 assignments must be approved for the student to participate in the exam.
- Aid
- All aids allowed
ChatGPT and other large language model tools are permitted as a dedicated source, meaning text copied verbatim needs to be quoted, the tool cited, and generally the specific use made of them needs to be described in the submitted exam.
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Exam period
-
Exam information:
The examination date can be found in the exam schedule here
The exact time and place will be available in Digital Exam from the middle of the semester.
- Re-exam
-
The second and third examination attempts are conducted in the same manner as the ordinary exam.
Reexam info:
The reexamination date/period can be found in the reexam schedule here
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
To obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
To obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes.
- Category
- Hours
- Lectures
- 28
- Class Instruction
- 42
- Preparation
- 70
- Project work
- 46
- Exam
- 20
- English
- 206
Kursusinformation
- Language
- English
- Course number
- ASDK20005U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Autumn And Block 2
- Studyboard
- Social Data Science
Contracting department
- Social Data Science
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Hjalmar Alexander Bang Carlsen (2-6a6542757165306d7730666d)
Se skema
Er du BA- eller KA-studerende?
Kursusinformation for indskrevne studerende