Information on individual educational components (ECTS-Course descriptions) per semester

Big Data

Degree programme International Business Administration
Subject area Business and Management
Type of degree Bachelor
Summer Semester 2024
Course unit title Big Data
Course unit code 025008042215
Language of instruction English
Type of course unit (compulsory, optional) Elective
Teaching hours per week 2
Year of study 2024
Level of the course / module according to the curriculum
Number of ECTS credits allocated 3
Name of lecturer(s) Eric KYPER
Requirements and Prerequisites


Course content
  • Data Mining
  • Data Advantages
  • Backup requirements
  • Security and societal as well as legal aspects related to big data collection and analysis
  • Business implications of trends in operational data processing.
Learning outcomes

Data is commonly referred to as the "oil of the 21st century". In many cases, the use of data is a source of significant earnings potential and competitive advantages. At the same time, a data architecture that is accessible from the outside harbours considerable dangers and risk potential. For students of all majors, not only for those in Digital Management and IT, this course provides the foundations to recognise and use the entrepreneurial potential of Big Data and to manage the associated risks.

Students will recognise general data mining techniques. They recognise threats to business security and identify business continuity requirements and backup plans in case of data loss. Students understand the primary objectives of Big Data and the associated potential benefits. Students will be able to perform a cost / benefit analysis of big data systems.

Planned learning activities and teaching methods

Interactive course with lecture, case studies, exercises in individual and group work, presentations and homework.

Assessment methods and criteria

Pre-assignment, participation during the seminar in the form of contributions and short presentations (individual or group assignments), post-assignment, individual weighting as determined by the instructors, announcement at the beginning of the semester



Recommended or required reading

Bhasin, M. L. (2006). Data Mining: A Competitive Tool in the Banking and Retail Industries. The Chartered Accountant, 588-594.

Bialik, C. (2013, March 1). Data Crunchers Now the Cools Kids on Campus. The Wall Street Journal.

Seifert, J. W. (2008). Data Mining and Homeland Security: An Overview. CRS Report for Congress.

Stein, J. (2011, March 10). Data Mining: How Companies Now Know Everything About You.

Mode of delivery (face-to-face, distance learning)

Classes with compulsory attendance