ASDK20001U Social Data Science Base Camp

Volume 2020/2021

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.


This three-part course introduces students to the interdisciplinary degree program of social data science. In the first two weeks, students are introduced to the group-based learning and working practices, which is a core element of the program. During this period, the students will conduct fieldwork-based ethnographic exercises and will be introduced to other qualitative methods and analytics. For the remaining five weeks of the course, students are introduced to the fundamentals of programming and data analysis in Python, covering topics such as variables, data structures, functions, and the social context of programming. In parallel, one day a week will feature lectures and exercises that will focus on elementary qualitative, quantitative, and integrated quali-quant methods. Throughout all seven weeks, a lecture series with speakers from within and outside the academy shall present examples and cases spanning the breadth and potentials of the novel field of social data science.

Learning Outcome


  • Define basic concepts within programming, statistics, and underlying mathematics, as well as qualitative and quali-quant methods.
  • Account for central themes and research questions within the field of social data science.
  • Gauge the landscape of applications and tools for data science and their respective purposes.
  • Account for the history and social implications of social data science methods.


  • Perform elementary programming tasks, including designing a program to collect data from an API.
  • Flexibly structure, merge, and reformat data coming from various sources and in different forms, including both quantitative and qualitative.
  • Conduct exploratory data analysis using descriptive statistics and visualization methods.
  • Reflect on the combination of ethnography with other social data science methods.


  • Evaluate and apply data science programming.
  • Work with and analyse data in interdisciplinary teams.
  • Communicate social data science insights using basic data visualization and appropriate statistical methods.
  • Identify and design potential solutions to common problems arising from new data sources, such as text and other unstructured data types.

The course will use one central textbook and supplementary readings as suitable. The weekly reading load will be 80-130 pages. Readings will be provided by the instructors.

Lectures, seminars, group work, exercises, coding tutorials and methods workshops.
  • Category
  • Hours
  • Lectures
  • 56
  • Preparation
  • 140
  • Exercises
  • 84
  • Project work
  • 132
  • Total
  • 412
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
Type of assessment
Individual written exam (portfolio). A series of short assignments will be administered throughout the course posing one or more sets of questions, with feedback provided during the course. All of the assignments must be submitted in revised and compiled form for assessment at the end of the course. All in all, the portfolio exam must be no longer than 20 standard pages.
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship

The second and third exam attempts run identical to the ordinary examination. The portfolio exam must be submitted individually and must be no longer than 20 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.