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University of Bayreuth, Press release No. 108/2021, 05 August 2021

New Supplementary Course of Studies from Autumn 2021: Data Literacy

Digitalisation is bringing about a social change that is affecting and permanently altering our individual everyday experience. Increasingly, we are encountering decisions made by algorithms. Doubtlessly, processes of machine learning and artificial intelligence are influencing our lives more and more. Consequently, the University of Bayreuth is offering a supplementary course of studies in Data Literacy from the 2021/22 winter semester.


In the Data Literacy supplementary course of studies, students acquire knowledge and skills to collect, manage, analyse, and apply data in a critical way. The supplementary study programme serves to provide a portion of the academic and professional skills required in the field, with a view to equipping students for professional tasks they will have to fulfil in a digitalised economy and society.

“Very important for the profession is the ability independently to carry out a targeted data analysis. Our graduates will be aware of the pitfalls lurking in data modelling and evaluation. Accordingly, they will select and apply methods that promote true insight. And they will be able to visualise and communicate results”, says Prof. Dr. Mirco Schönfeld, Programme Supervisor and Junior Professor for Data Modelling & Interdisciplinary Knowledge Generation.

Finally, communication skills have also become an important competency, says Schönfeld. Graduates will understand the requirements of algorithms on data, and recognize th modelling decisions behind them. Where-ever machine learning, artificial intelligence, or simple data analyses are involved, they will have a well-founded opinion and be able to make critical assessments. The supplementary course of studies consequently bundles basic concepts of computer science and the management of data, conveys ways of working and thinking in computer science, and promotes a critical perspective on the data management. Students will gain insight into central areas of data competency, which include the collection, management, and evaluation of data, as well as data-based knowledge and decision-making processes.

In order to be able to critically question algorithmically made decisions, an understanding of the underlying procedures, techniques and ways of thinking is essential. An insight into programming and data analysis and a critical examination of data practices are key to this. In addition, personal experience through independently conducted, data-driven research projects are most helpful in this regard. According to the programme supervisor: “The supplementary programme offers students from fields unrelated to computer science, in particular, the opportunity to acquire this knowledge and gain relevant experience. On the other hand, computer science students stand to gain specialised experience and expand on their existing knowledge. The supplementary course of studies will teach everyday skills, improve students’ communication skills, and prepares them for new challenges in professional life.”

What competencies are acquired?

“The most important competency is unquestionably the evaluation of data”, explains Schönfeld. “Students can learn programming languages for data evaluation and quickly produce great visualisations of their data. Indeed, programming languages do not represent any great hurdle – you don’t need a computer science degree to be able to use them with confidence. Students will learn about machine learning and artificial intelligence methods. And they will learn how to use such algorithms themselves. But even more importantly, they will be able assess the results of these methods competently and critically. Finally, students will gain important insights regarding the critical examination of data and algorithms in their everyday lives. They will understand that human decisions always play a role in algorithmically determined results.”

The supplementary course of studies offers...

  • Insights into key areas of data literacy - This includes the collection, management, and evaluation of data, as well as data-based cognition and decision-making processes.
  • Additional knowledge in the field of computer science for students of subjects unrelated to computer science. - The content of the supplementary programme includes, for example, the acquisition of a programming language.
  • Specialised experience, e.g. in the area of data analysis, for students of computer science. - The curriculum for students of computer science includes, for example, an independently conducted data analysis project.
  • Accreditation in the form of a certificate - Graduates of the supplemenray course of studies are awarded a certificate.

The modalities of the supplementary course of studies

Data Literacy consists of a compulsory module and several elective modules. The compulsory module depends on previous studies, whereas students can choose their elective modules themselves. Elective modules are, for example, data modelling & knowledge generation, data ethics & critical thinking, and dimensions of media & society. In the latter, for example, various aspects of media and society are presented in their historical context, reflected on in terms of media theory, and subsequently classified. In addition, the basics of legal framework conditions, such as copyright, trademark, teleservice, telemedia, youth protection, data protection, law of obligation, and criminal law are presented.

  • The supplementary programme of studies is open to all students of a master's programme or doctoral students of the University of Bayreuth – there is no application procedure.
  • The language is mainly German, but the supplementary programme can also be completed in English.
  • The supplementary course of studies is designed for four semesters in order to achieve the necessary 30 ECTS points.
  • The programme supervisor is Prof. Dr. Mirco Schönfeld, Junior Professor for Data Modelling & Interdisciplinary Knowledge Generation (see below for contact details).

Further information available at: www.dataliteracy.uni-bayreuth.de



Prof. Dr. Mirco SchönfeldJunior Professor for Data Modelling & Interdisciplinary Knowledge Generation

Phone: +49 (0) 921 / 55-4597
E-Mail: mirco.schoenfeld@uni-bayreuth.de
Zapf 3, Zimmer 3.23
Nürnberger Straße 38


Brigitte Kohlberg

Deputy Press & PR Manager, University Communications

Phone: +49 (0)921 / 55-5357
E-mail: brigitte.kohlberg@uni-bayreuth.de,

Office: Room 3.11, Building Central University Administration (ZUV)
Universitätsstraße 30, 95447 Bayreuth