Course content

This is a course about doing and applying mathematical models to business and economic problems using modern computational tools. The course makes extensive use of the Python programming language and associated scientific packages.

Topics covered include

• Introduction to computational techniques
• Derivation and integration
• Linear algebra:
  • Linear equation systems
  • Arrays
  • Determinants
  • Eigenvalues
  • Diagonalization
• Difference equations
• Differential equations
• Linear programming

Learning outcome

Knowledge

Students will:

• Understand how a mathematical model can be approximated on a computer.
• Master methods within linear algebra and should be able to apply this to analyze and solve linear equation systems
• Master matrix calculus, including the use of eigen values and eigen vectors, and apply this to solve mathematical problems, with special emphasis on applications in economics and business.
• Be able to apply integration methods and find approximate solutions computationally.
• Master solution methods for certain types of differential and difference equations, and should be able to analyze properties of the solutions, especially related to economic and business models
• Be able to solve linear differential equation systems with constant coefficients

Skills

Students will:

• Be comfortable programming in a mathematical model and finding an approximate computational solution(s)
• Be able to master a wide range of mathematical operations to solve tasks
• Be able to solve tasks where different parameters are included and be able to interpret and evaluate the solutions based on different values of the parameters

General competence

Students will:

• Be able to formulate a business or economic problem as a mathematical model
• Have a trained ability for logical thinking

Learning methods and activities

The course is organised around weekly "labs" that students work through. Students are also expected to work through weekly assignments.

Class time consists of a short introduction to a topic and time to work in groups and get feedback.

Specific conditions

Recommended previous knowledge

Introduction to Mathematics

Prior knowledge with programming in Python is helpful

Course materials

The final syllabus is given at the start of the semester.