Data Collection, Processing and Analysis (30 ECTS)
Course content
The purpose of this course is to provide students with an opportunity for collecting and working with data that is relevant in relation to the Master’s thesis. The course consists in participating in a data collection project such as running an experiment or scraping data from the internet. This includes preliminary processing and basic analysis. Students are only allowed to sign up for this course once in the course of the Master’s degree programme.
Elective course offered by 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.
NOTE: This is the course description for Data Collection 30 ECTS. The information in this course description is ONLY applicable to you if you are registered for 30 ECTS.
At the end of the course, students are able to:
Knowledge:
- Describe the use of different methods for doing research within social data science and the knowledge they produce.
- Define theoretical terms and research themes that can be used to understand relevant social data science problems within and empirical material.
Skills:
- Design large scale data collection process taking a point of departure in an independent problem formulation.
- Independently and critically collect relevant empirical material.
- Adjust the problem statement and research question and academically account for the adjustments.
- Systematically organize and structure the empirical material in accordance with research ethics.
- Document the collected data and account for how it has been structured.
Competences:
- Assess problem statement and research questions in relation to the empirical material different perspectives.
- Discuss ethical implications in regard to the data collection.
- Contemplate and assess the potential for applying the data for commercial and/or political purposes.
- Reflect critically on the methodological and analytical process of collecting data and applying it for and research purposes.
This course is conducted primarily as an independent study. At the beginning of the semester, the Head of Studies assigns students into supervision clusters. In the course of the semester, students must participate in workshops, organised by the cluster supervisor, focusing on presenting their social data science material and analysis. Before each workshop, students submit assignments that report on their progression of the data collection.
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
- 15 ECTS
- Type of assessment
-
Home assignment
- Type of assessment details
- Written exam submitted individually or in groups. Students in the same group must registered for the same number of ECTS. The exam must provide an overview of the collected data material and examples of it can be analyzed. For 30 ECTS credits, the written portfolio assignment must be no longer than 20 pages when written by 1 student and 30 pages when written by two students, who write together.
- Examination prerequisites
-
Exam registration requirements. To be eligible for exam, students must participate in the workshops and submit assignments before the workshops.
3 out of the 3 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
- 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
-
The second and third examination attempts are conducted in the same manner as the ordinary examination.
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
- Guidance
- 12
- Exam Preparation
- 812
- English
- 824
Kursusinformation
- Language
- English
- Course number
- ASDK20010U
- ECTS
- 15 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 semester
- Placement
- Autumn
- Studyboard
- Social Data Science
Contracting department
- Social Data Science
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Kristoffer Langkjær Albris
(17-7a817882837e757574813d707b718178824f827e7370823d7a843d737a)
Department of Anthropology
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