Statistical Analysis (Data Mining)

Understand data mining and use basic statistical methods to analyze data.

Statistical Analysis

(Data Mining)


  • Length of course: 4 weeks
  • Credit points: 2
  • Estimated workload: 60h
  • Type of course: self-paced, on demand


Business Intelligence refers to the procedures and processes for the systematic analysis of data in electronic form to enable better operational or strategic decisions. It helps companies to have more comprehensive knowledge of the factors affecting their business. Business intelligence is based on a broad variety of technologies. The ability to understand and master these technologies is a basis to become a data-driven company.

Focus of this Course

This course focuses on data mining as “the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques.”(Gartner). It introduces basic statistical/mathematical methods to analyze data.


  1. Introduction to Data Mining
  2. Descriptive Methods
  3. Prescriptive Methods

Learning outcome

  • Understand the concepts of Data Mining
  • Be able to use simple descriptive and prescriptive methods

Teaching method

  • Online lecture with exercises


  • Continuous assessment: Exercises

Recommended reading

  • Witten, Frank (2011): Data Mining, Practical Machine Learning Tools and Technicques, 3rd Ed., San Francisco, Elsevier.

Prof. Dr. Thomas Schmidt

is professor at Flensburg University of Applied Sciences for business computing, focusing on management information systems, business analytics, enterprise architecture and information logistics. He has 30 years of experience in IT and logistics and advised a large number of companies on strategy and implementation projects. He is an ACM member, member of the “Bundesvereinigung Logistik” and of the German-African Business Association.

Prior to his appointment as a professor, he started his career as a research associate at Fraunhofer Institute for Manufacturing Engineering and Automation, and later as Senior Management Consultant at CSC Ploenzke, responsible for the area of logistics. During this time, he did his PhD at the University of Bamberg.

Thomas Schmidt has 15 years of experience in Africa with projects in university partnerships with technology transfer in logistics and information technology. As Director of the Centre for Business and Technology in Africa, he founded the Namibian-German Centre for Logistics and operates partnerships in Cameroon, Kenya, Namibia and Ghana with successful cooperative Bachelor and Master programs. In addition to his African expertise, he taught and researched as a guest professor in the USA, Sweden, Finland, France and China.