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 custom-made 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.

Education

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.

Learning outcome

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


Knowledge

  • Explain the principles of empirical social science informing both quantitative and qualitative research.
  • Account for a broad variety of data collection methods used in the social sciences, 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 science research designs.
  • Collect primary data to answer research questions using survey and experimental methods.
  • Collect 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.

 

Competencies

  • Evaluate and critically reflect on published social 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.

Written
Collective
Continuous feedback during the course of the semester
ECTS
7,5 ECTS
Type of assessment
Portfolio
Type of assessment details
Portfolio exam written in groups. The portfolio consists of revisions to the earlier assignments that have been handed in and must be submitted by the end of the course. The final grade results from the combined assessment of the three assignments.
Exam registration requirements

During the course, students must in groups submit a set of compulsory assignments, each corresponding to one of the three blocks.

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
External censorship
Re-exam

An essay, written either in a group, or individually, on a subject pertaining to the course content and prescribed literature. The subject must be pre-approved by the course lecturer(s).

Criteria for exam assessment

The exam will be assessed on the basis of the learning outcome (knowledge, skills and competencies) for the course.

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 70
  • Exercises
  • 42
  • 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
Block 1
Capacity
80 students.
Studyboard
Social Data Science
Contracting department
  • Social Data Science
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Lau Lilleholt Harpviken   (3-7b7b794f7f82883d7a843d737a)
Saved on the 30-10-2023

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