Elementary Social Data Science
Course content
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 execution of research in the field of social data science. The course is structured in three constitutive blocks. The first block provides an introduction to different forms of data (e.g., readymade and custom-made data) as well as the art and challenges of collecting online data. The second block introduces prominent research designs (e.g., quantitative, qualitative, and mixed methods designs) and different (online) data collection methods (e.g., surveys and experiments). The third block introduces open science practices as well as principles and methods on how to conduct high quality research and establish high quality data (e.g., high validity and reliability). 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.
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:
- Explain the principles of empirical social science addressing 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.
Skills:
- 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.
Competences:
- 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.
Lectures, seminars, group-work and exercises.
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.
Students will be registered by the student administration.
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 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.
Length 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
-
An essay, written either in a group, or individually, on a subject pertaining to the course content and prescribed literature. The subject must be pre-approved by the course lecturer(s).
Reexamination registration requirements
Prior to the deadline for the reexamination registration, students must submit a set of compulsory assignments, each corresponding to one of the three blocks.
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
- 66
- Exam
- 0,5
- English
- 206,5
Kursusinformation
- Language
- English
- Course number
- ASDK20002U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Autumn And Block 1
- Studyboard
- Social Data Science
Contracting department
- Social Data Science
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Lau Lilleholt Harpviken (3-7b7b794f7f82883d7a843d737a)
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