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Supplementary study programmes at the University of Bayreuth

Data Literacy

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Content & Exams

In order to be able to critically question algorithmically made decisions, a basic understanding of the underlying procedures, techniques and ways of thinking is essential. Insights into programming and data analysis and a critical examination of data practices are necessary for this. In addition, having one’s own experience through independently conducted data-driven research projects is helpful.

The supplementary programme Data Literacy with a scope of 30 credit points offers students of computer science disciplines in particular the opportunity to acquire this knowledge and gain corresponding experience. It offers students of computer science the opportunity to gain specialized experience and deepen their existing knowledge. The programme not only teaches everyday skills but also prepares students for new challenges in professional life.

Completion of the supplementary programme Data Literacy is intended to demonstrate that students have the ability to independently recognize and critically classify concepts and problems from the aforementioned areas of data literacy. By teaching ways of thinking and working in computer science, the communication skills of students from disciplines that are not related to computer science are improved. Through the supplementary programme, students of all disciplines acquire special subject-specific knowledge and skills relevant to practice and everyday life. These skills are helpful for the transition into professional practice. They enable students to engage in the interdisciplinary exchange of ideas and problem solving in society and business, where increasingly data-driven decisions are made.

The modules in detail

Computational thinking

This is the compulsory module for students taking the Data Literacy supplementary programme without a B.Sc. Computer Science, B.Sc. Applied Computer Science or equivalent.

Students learn a programming language, learn the basics of computer science and algorithms, learn how computers work, etc.

From the courses offered at the University of Bayreuth, the module INF 504: Introduction to Computer Science is recommended for students of other subjects (see Module Handbook Computer Science). Alternatively, courses offered by the vhb, MOOCs, or comparable online courses can be taken. The examination board should be consulted about the choice of courses

 

Data analysis project

This is the compulsory module for students who pursue the supplementary programme Data Literacy with a B.Sc. Computer Science, B.Sc. Applied Computer Science or an equivalent degree. Students without the above-mentioned degrees can take this module as an elective.

Here, in consultation with the examination board, it is possible to submit an independently conducted data analysis project. Within the framework of such a project, for example, a contribution to a data analysis competition can be prepared.

 

Databases and information systems for students who are not familiar with computer science

Students learn theoretical concepts for modelling in the field of relational databases and information systems. Modules offered by the University of Bayreuth can be brought in here or suitable online courses.

This module is recommended for students who pursue the supplementary programme Data Literacy without a B.Sc. Computer Science, B.Sc. Applied Computer Science or an equivalent degree.

The vhb course "Relational Databases in Application" by Prof. Stefan Jablonski is recommended here. The examination board should be consulted about the choice of courses.

 

Data modelling and knowledge generation

Students learn about different methods for data analysis and knowledge generation - these include methods from the field of machine learning, data mining, text mining, social network analysis, and information visualization.
The students become aware of the requirements for the necessary data models that the different analysis methods entail.
The students understand to critically question data analyses, to concretely name the implicit modelling decisions, and to always evaluate analysis results against the background of these decisions.

 

Data ethics and critical thinking

Students highlight the cultural, ethical, and socio-technical challenges at the intersection of computer science, the social sciences, and society. Students critically explore topics such as big data, data science, data ethics, privacy, fake news, and discuss how data systems and algorithms can help solve societal problems.

 

Dimensions of media and society

Various aspects of media and society are presented in their historical context, reflected upon and classified in terms of media theory. In addition, the basics of legal framework conditions, e.g. copyright, trademark, teleservice, telemedia, youth protection, data protection, guilt, and criminal law are presented.
 

Applied Data Analysis: Social Network Analysis

The students complete research projects on topics of their own choice. Computer-assisted methods for data processing, data evaluation, visualization of results, etc. are used. The students work on the targeted selection and knowledge-promoting use of computer-supported methods from the field of social network analysis in a data evaluation process.

 

Applied Data Analysis Text Mining

Students carry out research projects on topics of their own choice. Computer-assisted methods for data preparation, data evaluation, visualization of results, etc. are used. Students work on the targeted selection and knowledge-promoting use of computer-supported methods from the field of quantitative text analysis in a data evaluation process.

 

Digitalium Generale

Here, in consultation with the examination board, there is a choice of any modules from the courses offered by the University of Bayreuth. Alternatively, courses offered by the vhb, MOOCs, or comparable online courses can be taken. The lecturers of the respective courses should be consulted as to whether participation is possible. The examinations in this module are not relevant for the final grade.


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