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Scientific Coordination

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

Administrative Coordination

Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Introduction to Computational Social Science with R

About
Location:
Mannheim B6, 4-5
 
General Topics
Course Level
Format
Software used
Duration
Language
Fees
Students: 500 €
Academics: 750 €
Commercial: 1500 €
 
Keywords
Additional links
Lecturer(s):  , Dr. Max Pellert

About the lecturer - Dr. 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 in person and will consist of a combination of lectures and practical exercises. 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 guidance from the lecturers, throughout the course. To make full use of the course participants should have knowledge of the very basic concepts of programming in R (for example write a loop themselves, read in a CSV file and be familiar with data types such as a data.frame), we link to a self-assessment test below (see Course Prerequisites). To gain that basic knowledge, several pointers to online crash courses on those very basics of R are linked below (see Course Preprequisites). Participants are expected to work through some of those materials before the course should they have never worked with R before at all or only had very limited experience with R.
 
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.


Target group

Participants will find the course useful if:
  • They are social scientists with very little or no experience with computational methods who would like to learn more about the 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
  • Be equipped  to use provided pointers to advanced materials to further improve their skills  
  •  
    Organizational structure of the course
    The course will consist of a combination of lectures and practical hands-on lab sessions. 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 (if you are unsure if your R knowledge is sufficient, here is a self-assessment test we prepared for you. In case you will see that the test is too difficult for you, we have also included links to several free online R crash courses that you should go through to prepare for our course https://seafile.ifi.uzh.ch/f/63542a5ab4be4d37846d/) )
  • Working command of English language
  • Knowledge of basic statistics (distributions, correlation)
  • Basic programming knowledge (variables, loops, conditions) in R (see the self-assessment test above)
  •  
    Software and hardware requirements
    For participants: 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.
     
    Participants should bring their own laptops and pre-install the following software/packages:
  • R
  • RStudio
  •  
    Recommended related courses
  • R 101 (Workshop, online, 31.08. - 01.09.2022)
  • Automated Web Data Collection with R (Fall Seminar, Mannheim, Week 2)
  • Network Analysis in R (Fall Seminar, Mannheim, Week 3)


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