https://studiegids.vu.nl/en/courses/2025-2026/S_DSVARUpon completion of this course, the student is able to:use various computational techniques in R: structuring digital data, visualization and systematic evaluation; critically reflect on the implications of the selection, structuring and manipulation of data for the outcome of their work; evaluate results critically and in a systematic manner; critically analyze other digital based research projects. They will be able to position their own work in the existing field of digital humanities and social analytics. collaborate in an advanced, interdisciplinary research groups; present their work in an academically convincing and ethical way for an interdisciplinary audience.The explosion of digital information and increasing efforts to digitize existing information sources has produced a deluge of data, such as digitized historical news archives, literature, policy and legal documents, political debates and millions of social media messages by politicians, journalists, and citizens. Graphs and charts let you explore and learn about the structure of the information you have collected. Good data visualizations enable you to communicate your ideas and findings. This course will offer analytical and practical training in digital visualization techniques using the open-source platform R. This course is placed in the broader scope of Digital Humanities and Social Analytics. In terms of critical reflection and skills this is a more advanced course within the Minor Digital Humanities and Social Analytics. An important part of the classes will entail practical training in the visualization of data: what are the "right numbers" to present, how to present uncertainty in data, which ties in a network are important enough? The course will teach you how to transform data to a visual: from a basic graphical display to animated and BBC-worthy graphics (e.g. see https://www.r-bloggers.com/create-data-visualizations-like-bbc-news-with This course invites you to develop visuals from many data sources, such as numerical data, textual data, networked data, etc. At the end of the course you will be able to use attractive visualizations to present your research results in both oral and written communications. After completion of this course you will possess knowledge of digital tools and opportunities of a field of research in order to continue to acquire computing skills andpursue further studies and / or a career that entails interdisciplinary collaboration, work with many types of data and media and involves high level critical and analytical skills.Lectures and seminarsIndividual and group assignments (40%), take-home exam (60%), both parts have to be evaluated with a sufficient grade to pass the course.Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press. (online version freely available) Additional scientific articles and book chaptersStudents who take the University Minor ‘Digital Humanities and Social Analytics’. As long as there are available places, we welcome other students of all disciplines, including international exchange students. Please contact the coordinator in advance.This course is part of the minor Digital Humanities and Social Analytics.This course is designed for students who take the minor Digital Humanities and Social Analytics. For other students it would be helpful to familiarize with the basics of digital data in advance. Please contact the instructors for more information and advice.