CC 303: Quantitative Methods

In today's world, leading companies rarely make complex decisions based on gut feelings, but instead systematically collect and analyze data, and model important relationships. Based on the empirical insights, decisions are also made systematically – with the help of optimization as decision support. For many business analytics methods – whether descriptive, predictive or prescriptive – linear algebra is a fundamental building block that is discussed in this course. Further important topics include the modeling and solution of practical optimization problems, including spreadsheet-based tools.

Learning outcomes
Students are supposed to learn about the fundamental methods of linear algebra and apply them to typical problems in business administration. In addition, structured approaches and analytical skills should be trained, e.g. the modeling of real decision-making situations as a mathematical optimization problem and their solution with the help of algorithms.
After completing the lecture, students should have subject-specific knowledge and be able to apply it to analyze, model and solve managerial problems in the respective academic disciplines.

Necessary prerequisites

Recommended prerequisites

Forms of teaching and learningContact hoursIndependent study time
Lecture2 SWS1 SWS
Exercise class2 SWS1 SWS
Tutorial2 SWS1 SWS
ECTS Credits3
Form of assessmentWritten exam (45 min.), optional bonus exercises during the semester
Restricted admissionno
Further information
Performing lecturer
Prof. Dr. Cornelia Schön
Prof. Dr. Cornelia Schön
OfferingFall semester
Duration of module 0.5 semesters
Range of applicationB.Sc. Bus. Adm.
Preliminary course work
Program-specific Competency GoalsCG 1, CG 2, CG 4
Graded yes
Literatures. syllabus
Course outline
  • Fundamentals of linear algebra
  • Systems of linear equations
  • Linear optimization, Simplex algorithm
  • Integer optimization
  • Practice applications