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Introduction to Computational Social Science
Graduate Program (& Advanced Certificate) Status
Course Description

Please note that this course will be offered in a condensed format, during the last 6 weeks (2nd half) of the term.

Social sciences undergo a rapid change. The data deluge due to the fantastic development of computers and information-communication technology opens unprecedented possibilities so that earlier inaccessible problems can be studied and understood, and even predictions can be made. These problems include the large-scale structure of society, multi-scale dynamics of human mobility, social contagion, filtering fake news and identifying echo chambers, urban dynamics and well-being in cities, and many more. The new challenges brought by using Big Data in social sciences require new methods and multi-disciplinary effort. As a result, a new field has emerged based on social science, computer science and complexity science, where computers are pivotal in designing and performing research. Techniques like data mining and processing, agent-based modeling and online experiments, statistical evaluation and theoretical analysis are equally part of it. The goal of the course is to give an introduction to this new, emergent field.

Learning activities and teaching methods

Students will have to follow the lectures, prepare homework and present a project work prepared in teams or individually. 

Learning Outcomes
  • Students will learn what computational social science is about, and what are the main techniques and representative research topics.
  • They will get acquainted with some data science tools and databases for studying social systems.
  • They will be able to formulate adequate research questions within computational social science and carry out small scale research projects.
Assessment
  1. Assessment type 1 (30 % of final grade). Students will get homework consisting of simple problems, which they will have to submit electronically.
  2. Assessment type 2 (60% of final grade). Students will have to prepare independent project work and present it in the last class.
  3. Assessment type 3 (10% of final grade). Students are expected to participate actively in class.
Prerequisites

This is an introductory course with no prerequisites. Still, programming and math skills are a plus to take advantage of the course contents:

  • Basic/intermediate programming skills, preferably in Python (e.g. familiarity with variables, loops and conditionals, functions, data structures, file I/O, etc.).
  • Intermediate/advanced math skills: Linear algebra, calculus and differential equations, probability theory and statistics.
Course Level
Doctoral
Academic Year
2023-2024
Term
Fall
US Credits
2
ECTS Credits
4
Course Code
DNDS 6014