Social Data Science Base Camp

Course content

This course introduces students to the interdisciplinary degree programme of Social Data Science. In the first week, students are introduced to the group-based learning and working practices, which are core elements of the degree program. For the rest of the term, students are introduced to the fundamentals of programming, data collection, and data analysis in Python including regression analysis. This will be combined with lectures and exercises that focus on elementary statistical modelling techniques and integrated quali-quant methods. Overall, the course will teach students the basic skills to program, collect and process data from a variety of online sources and structure them into a dataset, and to conduct basic analyses on that dataset.


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:


  • Define and explain how to use basic concepts within programming, including variables and data structures, control flow, and functions
  • Account for use cases of key Python libraries for data collection and analysis, including Pandas and MatPlotlib
  • Define basic concepts within statistics and underlying mathematics
  • Account for advantages and disadvantages of different quantitative approaches, in particular basic machine learning and regression


  • Perform elementary programming tasks in Python drawing on basic programming concepts
  • Navigate and draw on online and offline resources to debug Python programs
  • Use the basic toolkit to use Application Programming Interfaces for data collection and processing
  • Set up basic Python scripts for scraping and adjust them to various online sources
  • Flexibly structure, merge, and reformat data coming from various sources and in different forms, including quantitative and qualitative data
  • Conduct exploratory data analysis using descriptive statistics, visualization methods, and content analysis
  • Estimate regression models and explain the output


  • Work with and analyse data in interdisciplinary teams
  • Critically assess and reflect on their own and others’ coding practices
  • Communicate social data science insights using basic data visualization and appropriate statistical methods to relevant audiences
  • Integrate a netnographic approach with computational data collection

Lectures, seminars, group work, exercises, coding tutorials and methods workshops.

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.

Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
Type of assessment
Individual Written Exam.

The exam will consist of submitting code to collect and process data in order to produce a dataset of the student’s choosing, along with a description and reflection on how they constructed the dataset. The code must be in the form of a Jupyter Notebook. Within the Notebook, students will also be required to conduct a basic analysis on that dataset in accordance with the Learning Outcomes.
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
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
  • 56
  • Preparation
  • 140
  • Exercises
  • 84
  • Project work
  • 132
  • English
  • 412