Data Collection, Processing and Analysis (15 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.

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 15 ECTS. The information in this course description is ONLY applicable to you if you are registered for 15 ECTS.

Learning outcome

At the end of the course, students are able to:

Knowledge:

  • Describe the choice of method for doing research within social data science and the knowledge it produces.
  • Define theoretical terms and research themes that can be used to understand relevant social data science problems within and empirical material.

 

Skills:

  • Carry out a smaller data collection, taking point of departure in an independent problem formulation.
  • Organize the empirical material systematically, taking into consideration research ethics.

 

Competences:

  • Reflect critically on the methodological and analytical process of collecting data.
  • Assess problem statement and research questions in relation to empirical material.

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.

Oral
Individual
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
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 15 ECTS credits, the written portfolio assignment must be no longer than 10 pages when written by 1 student and 15 pages when written by two students, who write together.
Examination prerequisites

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
  • 6
  • Exam Preparation
  • 406
  • English
  • 412

Kursusinformation

Language
English
Course number
ASDK20009U
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   (17-6e756c7677726969687531646f65756c76437672676476316e7831676e)
    Department of Anthropology
Saved on the 01-05-2025

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students