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

• 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

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
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

Method of Assessment

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 chapters

Target Audience

Students 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.

Additional Information

This course is part of the minor Digital Humanities and Social

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. Please
contact the instructors for more information and advice.

General Information

Course Code S_DSVAR
Credits 6 EC
Period P2
Course Level 300
Language of Tuition English
Faculty Faculty of Social Sciences
Course Coordinator dr. M.A.C.G. van der Velden
Examiner dr. M.A.C.G. van der Velden
Teaching Staff dr. M.A.C.G. van der Velden
F. Loecherbach
M.J. Steijaert MSc

Practical Information

You need to register for this course yourself

Teaching Methods Seminar, Practical
Target audiences

This course is also available as: