Syllabus for Introduction to Computational Social Science

Introduktion till computational social science

  • 7.5 credits
  • Course code: 1DL007
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Computer Science G1N, Development Studies G1N, Sociology G1N, Political Science G1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2021-03-04
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2021
  • Entry requirements: General entry requirements
  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course the student shall be able to:

  • explain and apply different computational models, including agent-based models, social network analysis and computational text analysis, to study social phenomena and systems;
  • use these models with appropriate software tools;
  • evaluate and compare the suitability of different models to address social science problems;
  • make judgments with regard to relevant scientific, social and ethical aspects in the application of computational social science.

Content

The course introduces computational approaches to model human behaviour and social phenomena. Core concepts in computational social science are covered, such as observational studies (what types of data exist, possible biases and how to use data for modelling), basic concepts and techniques for running experiments (asking vs. observing, natural experiments, simulations, validity and generalisation) and discuss key issues such as ethical considerations.

The course has both a theoretical and a practical perspective, where students learn basic principles and also how to apply them in practice in three main areas:

  • social network analysis,
  • text analysis,
  • agent-based modelling and simulation.

Instruction

Lectures, lessons, labs and seminars.

Assessment

Written assignments (4 credits), active participation in obligatory seminars (1.5 credits) and project (2 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

The reading list is missing. For further information, please contact the responsible department.

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