NDAB22009U Numerical Methods (NuMe)

Volume 2024/2025

BSc Programme in Computer Science and Economics


Numerical methods provide the foundation for working with computer models for solving economic problems.

In the course, students will be introduced to methods from numerical analysis and applied mathematics, which are often used to solve economic real-life problems. The course includes both theoretical and practical components.

The course covers the most basic numerical methods, including numerical optimization, methods for solving non-linear equation systems, approximation of functions, interpolation methods, numerical integration, and differentiation. Likewise, students are introduced to a few selected advanced topics such as Monte Carlo methods.

Examples are used throughout the course which shows how numerical methods can be used for industrial task optimization, stock market analysis, job search, etc.

Students will be introduced to a high-level programming language such as Python and will be asked to implement a selection of the numerical methods on Python.

Learning Outcome

Knowledge of
•    Numerical Optimization,
•    Non-linear equation systems,
•    Approximation,
•    Differentiation and integration,
•    Monte Carlo simulation.
Skills to
•    Explain how optimization problems and non-linear equation systems can be solved using numerical methods,
•    Explain how numerical methods are used for approximation of functions, differentiation and integration,
•    Implement the numerical methods in a (general purpose) programming language and check their correctness.

Competences in
•    Working with open tasks where some data is missing,
•    Explaining what distinguishes "exact solutions" from "numerical approximation",
•    Using numerical methods to solve simple models within, for example, economics. 

1. Programming corresponding to the course Programming and problem solving (PoP)
2. Linear algebra corresponding to Linear algebra for computer scientists (LinAlgDat).
3. Mathematical analysis corresponding to one of the courses Introduction to mathematics (MatIntroNat) or Mathematical analysis and probability theory in computer science (MASD).
4. Probability Calculation and Statistics equivalent to Basic Statistics and Probability Calculation (GSS), Probability Calculation and Statistics (SS) or Modeling and Analysis of Data (MAD) plus Mathematical Analysis and Probability Theory in Computer Science (MASD).
Lectures and exercise classes.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 67
  • Exercises
  • 110
  • Exam
  • 1
  • Total
  • 206
Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
Continuous assessment based on 6 written submissions and related programming tasks.
The final grade is based on an overall assessment of the submissions.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Internal assessment.

The re-exam consists of two parts:

1) handing in the 6 written submissions and related programming tasks. PLease note clearly what revisions have been made.
2) an oral 15 min exam without preparation.

The final grade is based on an overall assessment.

Criteria for exam assesment

See Learning Outcome.