ASDK20010U Data Collection, Processing and Analysis (30 ECTS)

Volume 2026/2027
Education

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.

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.

Learning Outcome

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.
  • Category
  • Hours
  • Guidance
  • 12
  • Exam Preparation
  • 812
  • Total
  • 824
Oral
Individual
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
Credit
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 assesment

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.