Module manual


Computer Science in Business Applications


Basic IT, solid working knowledge in the functional areas of business administration, knowledge of Excel, statistics and ERP systems

Teaching methods Lecture/Excercise/Lab
Learning target / Competences

The support of operative business processes by IT systems is now standard in modern companies. In addition, newer IT technologies have become increasingly important for the digitalization of companies in recent years: The growing flood of data increasingly requires the systematic management of structured and unstructured information as well as analytical skills. Mobile devices not only allow information to be accessed and generated at any place and at any time, but also make entirely new business processes and business models possible. The module provides a summary of these developments.
Students develop an understanding of the benefits, opportunities and value contributions of these new technologies in the context of digitalization, and their significance for new business models.
Students are able to represent the view of the specialist department on issues relating to business analytics, mobile applications and information management as a competent contact person and to participate in the formulation of requirements and the selection of technologies.
Students are proficient in the practical use of the corresponding tools throughout the entire process (development of mobile applications, realization of analytical applications and use of ECM systems).

Duration 1 Semester
Hours per week 6.0
Classes 90 h
Individual / Group work: 180 h
Workload 270 h
ECTS 9.0
Requirements for awarding credit points

Module assessment: Practical work (PA) and written exam (K120)
Weighting: 1/3 PA, 2/3 K120

Responsible person

Prof. Dr. Tobias Hagen

Recommended semester 1. oder 2. Semester
Frequency Annually (ss)

Betriebswirtschaft (Master)
Wirtschaftsingenieurwesen (Master)


Mobile Applications

Type Lab
Nr. W1164
Hours per week 2.0
  • Foundations of mobile applications
  • Basic technologies such as HTML, CSS, Javascript
  • Basic "responsive web design" using the Bootstrap framework
  • Project task: Development of a responsive website using Bootstrap
  • Characteristics and different types of mobile applications (with special attention to platform-independent mobile applications)
  • Introduction to the Apache Cordova framework
  • Project task: Development of an app for smartphones and tablets using Apache Cordova
Literature Online documentation for HTML, CSS, JavaScript

Information Management

Type Lecture
Nr. W1166
Hours per week 2.0
  • Basic information management
  • Strategic apects of information management
  • Information economy
  • Document management / enterprise content management
  • Portals and data integration
  • Information retrieval
  • Master data management

Krcmar, Helmut (2015): Informationsmanagement. Berlin, Heidelberg: Springer Berlin Heidelberg.
Lewandowski, Dirk (2015): Suchmaschinen verstehen. Berlin, Heidelberg: Springer Berlin Heidelberg; Springer Vieweg.
Götzer, Klaus; Maier, Berthold; Schmale, Ralf; Rehbock, Klaus; Komke, Torsten (2014): Dokumenten-Management. Informationen im Unternehmen effizient nutzen. 5., rev. ed. Heidelberg: Dpunkt-Verl.
Steinbrecher, Wolf; Müll-Schnurr, Martina (2014): Prozessorientierte Ablage. Dokumentenmanagement-Projekte zum Erfolg führen. Praktischer Leitfaden für die Gestaltung einer modernen Ablagestruktur. Wiesbaden: Gabler Verlag.
Henrich, Andreas (2008): Information Retrieval 1. Grundlagen, Modelle und Anwendungen. Otto-Friedrich-Universität Bamberg. Available online: (last reviewed on 11 Feb 2021).

Business Analytics

Type Lecture/tutorial
Nr. W1119
Hours per week 2.0
  • Predictive analytics and machine learning
  • CRISP process
  • Exploratory data analysis
  • Supervised learning: regression and classification
  • Non-supervised learning: clustering and association analysis
  • Practical application of the methods with KNIME
  • Analytical applications

Dorer K, Hagen T, Lauer T, Sänger V, Trahasch, S (2020) Einführung in Maschinelles Lernen (online script) Berthold, M. R., Borgelt, C., Höppner, F., Klawonn, F., and Silipo, R. (2020). Guide to Intelligent Data Science. Springer International Publishing.
Müller, R. M., and Lenz, H.-J. (2013). Business Intelligence. Springer Berlin Heidelberg.