The course takes place in period

1 (2022-08-01 to 2022-10-23)


Professional studies

Teaching language


Type of course


Cycle/level of course


Recommended year of study


Total number of ECTS

5 cr

Competency aims

The aim of the course is to theach the student to
analyze large data sizes with mathematical tools in
a computer enviroment.

Learning outcomes

At the end of the course the student is expected
to be able to plan an experiment with high signal
to noise ratio and extract from the data
information. Infromation extraction use
filtering, time series analysis, derivation and
integration as well as modell fitting procedures

Course contents

  • production of data (material technical) and
    determination of noise level and error margins
  • extraction of information from råw data with
    mathematical tools(scilab, matlab, octave, excel)

Prerequisites and co-requisites


Previous course names


Recommended or required reading

material is distributed during lectures

Study activities

  • Lectures - 60 hours
  • Individual- and group instruction - 30 hours
  • Practical exercises - 40 hours
  • examen - 5 hours


  • Total workload of the course: 135 hours
  • Of which autonomous studies: 135 hours
  • Of which scheduled studies: 0 hours

Mode of Delivery

Participation in tuition

Assessment methods

  • Exams (written-, oral-, home-)
  • Essays, reports, productions and portfolio

Assessment requirements

theory exam 70% and one report about different own
data analysis 30%

grade 1 50-59%
grade 2 60-69%
grade 3 70-79%
grade 4 80-89%
grade 5 90-100%


Herrman Rene


Herrmann Rene

Group size

No limit

Assignments valid until

12 months after course has ended

Course enrolment period

2022-08-10 to 2022-09-06

Assessment methods

  • Date of examination will be announced later - Exams
  • Date will be announced later - Reports and productions

Course and curriculum search