Scientific Computing for Data Analysis

5 credits

Syllabus, Bachelor's level, 1TD352

A revised version of the syllabus is available.
Education cycle
First cycle
Main field(s) of study and in-depth level
Computer Science G2F, Technology G2F
Grading system
Pass with distinction, Pass with credit, Pass, Fail
Finalised by
The Faculty Board of Science and Technology, 3 March 2022
Responsible department
Department of Information Technology

Entry requirements

60 credits including a programming course in Python (eg Computer programming I) and Algebra and Geometry/Linear Algebra I. Participation in one of the courses Introduction to Scientific Computing and Scientific Computing I. Participation in Probability and Statistics. Participation in Linear Algebra II/Linear Algebra for Data Analysis or the course can be taken in parallel.

Learning outcomes

On completion of the course, the student should be able to:

  • account for and perform tasks that require knowledge of the key concepts included in the course;
  • describe, use and implement the algorithms included in the course;
  • analyze the computational and memory complexity of different algorithms;
  • solve technical and scientific problems given the mathematical model, by structuring the problem, choosing the appropriate numerical method, and generating a solution using software and your own code;
  • present, explain, summarize, evaluate and discuss solution methods and results in a small report.


Stochastic models, Monte-Carlo methods, Inverse Transform Sampling (ITS), stochastic simulation, Gillespies algorithm. Least square approximation with application to linear systems and regression models. QR factorization and Householder transforms. Methods for eigenvalues and eigenvectors (power method and QR method). Singular value decomposition (SVD) and Principal component analysis with applications. Important key concepts included in the course include stochastic/deterministic model and method, rotations and reflections, matrix factorization, singular values.


Lectures, laboratory exercises, problem solving sessions.


Written exam (3 hp). Problem solving and assignments with a written report (2 hp).

If there are special reasons for doing so, an examiner may make an exception from the method of

assessment indicated and allow a student to be assessed by another method. An example of special

reasons might be a certificate regarding special pedagogical support from the disability coordinator of the