Course content

The module introduces students to fundamental concepts in computer science. This includes, amongst other things, teaching the basics of number systems, text encoding, file formats, digitisation and digital media, information theory, image, audio and video compression, computer architectures and operating systems, formal languages and automata, as well as networks, the Internet and the World Wide Web.

This module provides students with knowledge of user-centred software design and, in connection with this, aspects of human information behaviour. Students learn how software development processes can be designed to systematically identify users’ needs and expectations and integrate them into the development process. This includes methods of requirements analysis, usability testing, prototyping and evaluation procedures to ensure a high level of user-friendliness.

This module is designed to provide students with a basic understanding of programming. This includes teaching the fundamental concepts of programming, knowledge of how to use Python packages for natural language processing, as well as knowledge of how to use Python for visualising datasets.

This module provides students with a basic understanding of machine learning. Students will gain an understanding of the theoretical foundations and operational principles of various machine learning methods, including supervised and unsupervised learning, as well as deep learning techniques. A key focus is on the practical application of existing tools and frameworks (e.g. WEKA) to perform data analysis and develop machine learning models. In addition, students will develop the skills to critically evaluate and interpret the results of the analysis, enabling them to correctly assess their significance and relevance within the respective field of application.

This module covers the fundamentals of data collection and data analysis. This includes, amongst other things, qualitative and quantitative methods of data collection (e.g. questionnaires), descriptive methods of data analysis (e.g. the presentation of empirical distributions) and inferential statistical methods of data analysis (e.g. the G-test).

This module aims to impart knowledge of computer-assisted processing of written language. It covers key techniques in automated language processing, including methods of syntactic analysis, techniques of semantic composition, and the modelling and processing of metadata for machine processing. Particular emphasis is placed on the application and reflection on these methods in the context of linguistic issues. As part of the course, students deepen their knowledge of information retrieval, information modelling and processing, as well as the practical use of relevant technologies.

Building on existing Python programming skills, this module focuses on teaching the skills required to develop web applications. This includes, amongst other things, teaching the basics of HTML and CSS, providing an introduction to JavaScript and jQuery, and covering advanced Python skills for developing web applications (e.g. Django).

This module focuses on the digitisation of both data and workflows for humanities scholars. Possible topics include the digitisation and indexing of analogue data collections, the application of computer-assisted research methods in the humanities (e.g. digital art history), and the user-centred design of humanities tools (e.g. to support linguistic annotation).

In this module, students will work independently on a project focusing on the computational processing of (written) language. This includes, for example, issues relating to stylistic analysis (such as the automatic attribution of texts to authors) as well as corpus linguistic challenges (such as the construction of a searchable, digital corpus of a specific language or language variety).

This module focuses on the analysis and understanding of human information behaviour. This includes, for example, the quantitative analysis of large datasets from social media (e.g. Twitter) and qualitative studies on information seeking (e.g. at information kiosks in museums).