Data Science: Visualization and Analytics in R

Vrije Universiteit Amsterdam

Course Description

  • Course Name

    Data Science: Visualization and Analytics in R

  • Host University

    Vrije Universiteit Amsterdam

  • Location

    Amsterdam, The Netherlands

  • Area of Study

    Information Studies, Research

  • Language Level

    Taught In English

  • Course Level Recommendations


    ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    • 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.

    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 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.

    Lectures and seminars 

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

    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. 

Course Disclaimer

Courses and course hours of instruction are subject to change.

Some courses may require additional fees.


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