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
custommade
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
-
Oral exam on basis of previous submission
- Type of assessment details
- Free home assignment with oral examination.
Students can hand in their assignment and take the oral examination individually or in groups of max. 4 students.
Length of home assignment: 10 pages
Length of oral exam: 20 min. for 1 student, 30 min. for a group of 2, 40 min. for a group of 3, 45 min. for a group of 4.
The written assignment will account for 2/3 of the final grade, and the oral examination will account for 1/3. - Examination prerequisites
-
3 out of the 4 assignments must be approved for the student to participate in the exam.
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.
- Aid
- Only certain aids allowed (see description below)
All aids allowed for the free home assignment.
No aids allowed for the oral examination.
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
- No 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
-
Same as the ordinary exam.
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 Coordinators
- Lau Lilleholt Harpviken (3-73737147777a8035727c356b72)
- Sandro Ferreira Sousa (3-7c6f7c497c786d6a7c37747e376d74)
Se skema
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Kursusinformation for indskrevne studerende