ASDK20002U Elementary Social Data Science

Volume 2020/2021
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.

Content

This course provides students with a comprehensive introduction to the central concepts and research processes that inform the field of social data science. The course introduces and offers basic experience with important research methods of social data science, covering classic approaches such as experiments and surveys, new approaches such as “big data” analysis and machine learning, as well as their intersection. The course structure follows an ideal form of the research process with three interconnected blocks. The first block reviews the research design stage, including generating research questions and hypotheses as well as evaluating different research approaches. The second block surveys prominent tools for data collection. The third block provides an overview of the methods available to analyse quantitative data, centring regression analysis. In all, the course prepares students to conduct basic social data science research and to acquire advanced methods in later courses.

Learning Outcome

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 the fundamental data analysis methods in social data science, as well as their strengths and weaknesses.
  • Explain common criteria for high-quality, replicable social science research.

 

Skills

  • Read, interpret, and produce pre-registrations of social science research.
  • 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 , building on the skills from the Social Data Science Base Camp.
  • Develop social science research designs including generating research questions, operationalizing theoretical concepts, and following best practices.
  • Conduct basic qualitative and quantitative data analysis, particularly basic regression analyses.

 

Competencies

  • Evaluate and critically reflect on published social science research by applying the highest international standards.
  • Identify opportunities to use digital data sources and apply computational methods to generate novel social scientific insights.
  • Plan and conduct high-quality social data science research projects, encompassing the research design, data collection, and data analysis stages.

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.

Lectures, seminars, group-work and exercises.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 70
  • Exercises
  • 42
  • Project work
  • 66
  • Total
  • 206
Written
Collective
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Portfolio
Portfolio exam written by groups of 3 or 4 students. During the course, students will submit a set of short compulsory assignments, corresponding to the three blocks: Research design, data collection and data analysis, and will receive feedback from the instructors. For the final exam, the three assignments must be submitted in revised and compiled form for assessment at the end of the course.

The total length of the portfolio exam must not exceed:
• For three students: 20 standard pages
• For four students: 25 standard pages
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Re-exam

The second and third exam attempts run identical to the ordinary examination. It will be possible to do the re-examination individually or in groups. If the portfolio exam is written individually, the maximum length is 10 standard pages.

Criteria for exam assesment

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