The course is included in these curricula and study modules
- Materials processing technology 2014 (swe) - Design
- Materials processing technology 2015 (swe) - Design
- Materials processing technology 2016 - Methodology and degree thesis
- Materials processing technology 2017 - Methodology and degree thesis
- Materials processing technology 2018 - Methodology and degree thesis
- Materials processing technology 2019 - Methodology and degree thesis
- Materials processing technology 2020 - Methodology and degree thesis
- Materials processing technology 2014 - Design
- Materials processing technology 2015 - Design
- Materials processing technology 2016 - Methodology and degree thesis
- Materials processing technology 2017 - Methodology and degree thesis
- Materials processing technology 2018 - Methodology and degree thesis
- Materials processing technology 2019 - Methodology and degree thesis
- Mechanical and sustainability engineering 2020 - Methodology and degree thesis
The course takes place in period
1 (2023-08-01 to 2023-10-22)
Level/category
Teaching language
English
Type of course
Compulsory
Cycle/level of course
First
Recommended year of study
4
Total number of ECTS
5 cr
Competency aims
Natural science
SDGs in focus: #4 QUALITY EDUCATION
Learning outcomes
Knowledge of measurement errors and error
propagation (knowledge)
Capacity to extract parameters from measurement
data using least square algorithm (skill)
Calculate from time series fourier transform and
evaluate spectra (skills)
Course contents
Data, Information and noise, error propagation,
minimisquare method and parameter extraction,
fouriertransform of time series.
Prerequisites and co-requisites
mathematics
Previous course names
none
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
Workload
- 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-)
Assessment requirements
2 exams, literature exam 30%, Calculation exam 70%
grade 1 50-59%
grade 2 60-69%
grade 3 70-79%
grade 4 80-89%
grade 5 90-100%
Teacher
Herrman Rene
Examiner
Herrmann Rene
Home page of the course
Group size
No limit (30 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2023-08-10 to 2023-09-06
Assessment methods
Date of examination will be announced later - Exams
| Date | Time | Room | Title | Description | Organizer |
|---|---|---|---|---|---|
| 2023-09-01 | 13:00 - 16:00 | E385 | Data analysis | Herrman Rene | |
| 2023-09-08 | 09:00 - 12:00 | E385 | Data analysis | Herrman Rene | |
| 2023-09-11 | 13:00 - 16:00 | E383 | Data analysis | Herrman Rene | |
| 2023-09-20 | 09:00 - 12:00 | D399 | Data analysis | Herrman Rene | |
| 2023-09-22 | 13:00 - 16:00 | E385 | Data analysis | Herrman Rene | |
| 2023-09-25 | 09:00 - 12:00 | D399 | Data analysis | Herrman Rene | |
| 2023-09-27 | 13:00 - 16:00 | D399 | Data analysis | Herrman Rene | |
| 2023-10-03 | 09:00 - 12:00 | E383 | Data analysis | Herrman Rene | |
| 2023-10-04 | 13:00 - 16:00 | E385 | Data analysis | Herrman Rene | |
| 2023-10-11 | 13:00 - 16:00 | F365 | Data analysis | Herrman Rene | |
| 2023-10-13 | 13:00 - 16:00 | E383 | Data analysis | Herrman Rene | |
| 2023-10-25 | 13:00 - 15:00 | D4106 | Data Analysis Exam 1 | Herrman Rene | |
| 2023-11-01 | 13:00 - 16:00 | E385 | Data Analysis Exam 2 | Herrman Rene |