• dh.elte.hu@gmail.com
  • 1088 Budapest, Múzeum krt. 6-8.

Please find course descriptions in Hungarian here.

Text encoding and the Text Encoding Initiative (ENG, HUN, ESP, FR)

Responsible for Course: Gábor Palkó, PhD. habil
 Bence Vida, Tímea Bajzát (Centre for Digital Humanities)
Start date: April 12, 2021
Due date: May 3, 2021

The purpose of the course is to give an insight into the basic storing and processing format of the texts of literature and digital humanities: TEI XML. The program was put together in a way to be suitable both for beginners and for a bit more experienced students, as well.

In the first theoretical module students can learn about the history and use of markup languages. After that, the basics of using XML (eXtensible Markup Language) format and DTD (Document Type Definition) will be mastered. Finally, the last module provides more insight and practice into the specifics and rules of TEI (Text Encoding Initiative) coding.
After completing the course, students will be able to use the main text storage tool in digital humanities, giving them the opportunity to engage in practical work such as digital philology or text publishing.

Conditions for completing the course:

Students will be graded through minor assignments completed during the course and a final own work. Through Microsoft Teams, once a week questions regarding the course material or the assignments can
be discussed.

Reading list:

Burnard, Lou, What is the Text Encoding Initiative? How to add intelligent markup to digital resources, Marseille, 2014.
Hockey, Susan M. Electronic Texts in the Humanities: Principles and Practice. Oxford; New York: Oxford University Press, 2000.
Kirschenbaum, Matthew G. Mechanisms: New Media and the Forensic Imagination. Cambridge Mass: MIT Press, 2007.
Renear, Allen, Text Encoding, in: A Companion to Digital Humanities, ed. Susan Schreibman, Ray Siemens, John Unsworth. Oxford: Blackwell, 2004.
Scholen, David and Scholen, Sandra, Beyond Gutenberg: Transcending the Document Paradigm in Digital Humanities, in: Digital Humanities Quarterly, Vol. 8 No. 4. 2014.

Data Visualization (ENG)

Responsible for Course: Gábor Palkó, PhD. habil
Assistants: Mária Timári, Péter Horváth (Centre for Digital Humanities)
Start date: April 12, 2021
Due date: May 3, 2021

Data Visualization is a course that teaches how to create visualizations that effectively communicate the meaning behind data to an observer through visual perception. We will learn how a computer displays information using computer graphics, and how the human perceives that information visually. We will also study the forms of data, including quantitative and non-quantitative data, and how they are properly mapped to the elements of a visualization to be perceived well by the observer. We will briefly overview some design elements for effective visualization, though we will not focus on the visual design needed to make attractive and artistic visualizations. This course does not require computer programming, but computer programming can be used to complete the exercises. The course will conclude with the integration of visualization into database and data-mining systems to provide support for decision making, and the effective construction of a visualization dashboard.

The course is comprised of the following elements:

  • Lecture videos. In each module the concepts you need to know will be presented through a collection of short video lectures. You may stream these videos for playback within the browser by clicking on their titles or download the videos. You may also download the slides that go along with the videos.
  • Programming assignments. There are two required programming assignments for the class. The first programming assignment is to create a visualization of numerical data, and the second programming assignment is to create a visualization of non-numerical data (e.g., a network or a hierarchy). For each assignment, sample data will be provided, but you are encouraged to find your own data. Your goal will be to present that data in a visualization that helps the observer to better understand what the data represents. The programming assignments will be peer graded based on rubrics that measure how well the course’s methods have been applied to the visualization of the data.

Conditions for completing the course:

Completing the course on the Coursera site begins with listening carefully to the videos, and reading related materials. It is recommended to take notes, as the examination will not be about solving tests between parts of the course (these are not mandatory, they only give students the opportunity to practice), but an online examination after completing the course.
In addition, two project tasks (visualization of numerical and non-numerical data) are required for completion, during which students can apply what they have learned. More information on these tasks can be found on the webpage.
Through Microsoft Teams, once a week questions regarding the course material or the assignments can be discussed.

Reading list:

Tamara Munzner, Visualization Analysis and Design, CRC Press, 2014.
Colin Ware, Information Visualization Analysis for Design (3rd Edition), Elsevier, 2013.

Only one of the courses listed needs to be taken. To apply, please click here.
The application deadline is April 9.