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.
After completing the course, the student is expected to be able to:
Knowledge:
- Overview of the potential data, techniques and methods for a
- social data science project
- Formulate a relevant and realistic social data science problem
- statement
Skills:
- Design and conduct a large-scale data collection process
- Prepare, prioritize and organize the data systematically
- Process the collected data using selected social data science
- techniques and methods
- Systematically organize and structure the empirical material in
- accordance with research ethics, compliance criteria, and best
- data management practices.
- Document the entire workflow, including problems encountered
- and methodological decisions
Competences:
- Independently plan and conduct a social data science data
- collection and processing project
- Reflect on methodological, ethical, and practical choices and
- challenges encountered when conducting a data collection and
- processing project.
- Contemplate and assess the potential for applying the data for
- commercial and/or political purposes and/or opensourcing the
- data for further research.
- Discuss ethical implications in regard to the data collection,
- including unintended consequences and grey areas.
This course is first and foremost an independent study. At the
onset,
students are assigned into supervision clusters. During the course,
students must partake in three workshops organised by the cluster
supervisor(s), where they present and reflect on the data
collection and
processing process. Prior to the first two workshops, students must
submit reports on the progression of their work on the
data.
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
- 30 ECTS
- Type of assessment
-
Home assignment
- Type of assessment details
- Home assignment submitted individually or in groups of two
students. Students in the same group must be registered for the
same number of ECTS. The home assignment must contain all the
prerequisite assignments handed in during the course and an
overview of the collected data material.
The home assignment must be no longer than 20 pages when written by 1 student and 30 pages when written by two students. - Examination prerequisites
-
Exam registration requirements. To be eligible for the 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
- 30 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
(17-70776e7879746b6b6a7733667167776e7845787469667833707a336970)
Department of Anthropology
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