Scientific Coordination
Dr.
Marlene Mauk
Tel: +49 221 47694-579
Marlene Mauk
Tel: +49 221 47694-579
Administrative Coordination
Claudia O'Donovan-Bellante
Tel: +49 621 1246-221
Tel: +49 621 1246-221
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Introduction to Computational Social Science with R
About
Location:
Mannheim B6, 4-5
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
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:
Learning objectives
By the end of the course participants will:
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
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:
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