GESIS Training Courses
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Scientific Coordination

Dr.
Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Loretta Langendörfer M.A.
Tel: +49 221 47694-143

Introduction to Computational Social Science with Applications in R

Lecturer(s):
Dr. Aleksandra Urman, Max Pellert

Date: 13.09 - 17.09.2021 ics-file

Location: Online via Zoom

About the lecturer - Dr. Aleksandra Urman

About the lecturer - Max Pellert

Course description

The course will provide an overview of the methods used in the field of computational social science (CSS) and their real-world applications. It will include both theoretical explanations of different methods and hands-on practical exercises through which the participants will be able to apply the discussed techniques in R. The course is aimed at participants with no or little experience with computational methods. Within the course, topics such as web scraping, foundations of computational text analysis, data visualization and ethical aspects of CSS will be covered. The course will take place online and will consist of pre-recorded video lectures combined with live Q&A sessions, exercises and project work. By the end of the course, each participant will have practical experience in R in retrieving web data, applying basic text analysis techniques to it, and visualizing the results. The participants will gain this experience through supervised practical exercises as well as through group projects on which they will work semi-independently, with the guidance from the lecturers, throughout the course.


Keywords



Target group

Participants will find the course useful if:
  • They are social scientists with little or no experience with computational methods who would like to learn more about computational social science methods and potentially use them in their research


  • Learning objectives

    By the end of the course participants will:
  • Be able to define what constitutes the field of computational social science and know which methodologies are commonly utilized in the field as well as which types of research questions can be handled using these methodologies
  • Be familiar with the major ethical aspects of conducting computational social science research
  • Have hands-on experience gathering digital trace data from online sources through direct web scraping and APIs using R
  • Know about the basic computational text analysis methods and have practical experience utilizing some of them using R
  • Be able to visualize their data using various techniques in R 
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    Organisational Structure of the Course
    The course will consist of a combination of pre-recorded lectures, live Q&A sessions and practical hands-on lab sessions that will take place live online. The lab sessions will consist of two components. The first one is practical scripted exercises related to a specific topic that the participants will be guided through by the lecturers. The second one involves semi-independent group work on the side of the participants and will be constituted by a group project in which the participants will apply the skills gained studying different topics covered in the course. Throughout this project the participants will be supported through individual consultations with the lecturers.


    Prerequisites

  • Basic knowledge of R (in addition, links to online tutorials for those who need to refresh their R skills will be provided to participants ahead of the course)
  • Knowledge of basic statistics (distributions, correlation)
  • Basic programming knowledge (variables, loops, conditions), preferably in R.
  • Participants may consider taking the following courses to refresh their R skills:
  • R crash course by David Garcia: https://dgarcia-eu.github.io/SocialDataScience/1_Introduction/015_RCrashCourse/RCrashCourse.htmla
  • SICSS boot camp course: https://sicss.io/boot_camp
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    Software requirements
    All the participants should have R and RStudio installed on their laptops, it's highly preferable that R is updated to the latest version. We will let participants know about specific packages necessary to install shortly before the course, and, if necessary, will help them with the specific package installation problems on Day 1 of the course. The lecturers are most familiar with Linux environments (e.g., Ubuntu or Debian) to run R and RStudio, but they can also provide support for Windows and MacOs.
    The participants will also need Zoom installed as the online live sessions will be held on Zoom.