Data Science: Visualization and Analytics in R

COURSE OBJECTIVE
• Students will master various computational techniques in R: structuring digital data, visualization and systematic evaluation.
• Students are able to critically reflect on the implications of the selection, structuring and manipulation of data for the outcome of their work. They are able to evaluate results critically and in a systematic manner.
• Students will be able to 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.
• Students are able to collaborate with advanced research groups, with other disciplines, manage group processes, and communicate results to a larger audience (final presentation). They will be able to present their work in a both academically convincing and ethical way for an interdisciplinary audience.
• Students possess knowledge of digital tools and opportunities of a field of research in order to continue to acquire computing skills and
pursue 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.

COURSE CONTENT
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 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.

TEACHING METHODS
Lectures and seminars 

TYPE OF ASSESSMENT
Group assignments (40%), take-home exam (60%)

RECOMMENDED BACKGROUND KNOWLEDGE
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.