From Data to Narrative via Computational Communication Science - Interactive curriculae of TU Ilmenau
The interactive curriculae provide information on the degree programmes offered by the TU Ilmenau.
Please refer to the respective study and examination rules and regulations for the legally binding curricula (Annex Curriculum).
You can find all details on planned lectures and classes in the course catalogue.
Please note that this page is no longer updated. All modules and study plans from PO version 2021 onwards (Bachelor and Master study programs) are now available on the Campus Portal.
| module properties From Data to Narrative via Computational Communication Science in degree program Master International Business Economics 2021 | |
|---|---|
| module number | 201336 |
| examination number | 2500659 |
| department | Department of Economic Sciences and Media |
| ID of group | 2559 (Kommunikationswissenschaft mit Schwerpunkt Computational Communication Science) |
| module leader | Prof. Dr. Emese Domahidi |
| term | summer term only |
| language | Englisch |
| credit points | 5 |
| on-campus program (h) | 22 |
| self-study (h) | 128 |
| obligation | elective module |
| exam | alternative examination performance |
| details of the certificate | Part 1: |
| link to Moodle course | |
| teacher | Prof. Dr. Emese Domahidi, Dr. Jingyuan Yu |
| signup details for alternative examinations | This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered. The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer. |
| maximum number of participants | |
| previous knowledge and experience | Basic knowledge of R programming, prior attendance to "Introduction to Computational Communication Science" is encouraged |
| learning outcome | By the end of this course, students are able to craft data-driven news articles by collecting, analyzing, and visualizing real-world data using R. They have learned to extract and interpret meaningful, newsworthy insights from datasets such as those from the German Federal Statistical Office and the United Nations Statistics Division. Students have developed the ability to interpret descriptive and statistical analyses in a non-technical manner and effectively communicate their findings to the public. They also gained hands-on experience in building interactive dashboards (e.g., via R Shiny) to facilitate discussion and engagement with both their peers and the public. Beyond technical skills, the course enabled students to critically examine social, economic, and political issues from a quantitative and computational perspective. Finally, students have refined their journalistic writing skills, learning how to convey complex data-driven narratives in a clear and compelling way. |
| content | This course introduces students to the principles and practices of data journalism, emphasizing the role of data in storytelling and investigative reporting. Students will learn how to source, clean, and analyze datasets to create news reports. They will also acquire skills in data visualization to effectively present their findings. As part of the course, students will produce a report that combines their data analysis, visualizations, and written insights. Students will also defend their work in an oral examination, demonstrating their ability to communicate complex findings. By the end of the course, students will have a portfolio of work showcasing their ability to use data to tell stories. |
| media of instruction and technical requirements for education and examination in case of online participation | Reading work, moodle, lecture script, course materials |
| literature / references | Will be announced each semester |
| evaluation of teaching | |