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 (2020-08-01 to 2020-10-25)
Level/category
Teaching language
English
Type of course
Compulsory
Cycle/level of course
First
Recommended year of study
3
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
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-)
- Essays, reports, productions and portfolio
Assessment requirements
theory exam 60% and one report about different own
data analysis 40%
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 (28 students enrolled)
Assignments valid until
12 months after course has ended
Course enrolment period
2020-08-10 to 2020-09-06
Assessment methods
- Date of examination will be announced later - Exams
- Date will be announced later - Reports and productions
| Date | Time | Room | Title | Description | Organizer |
|---|---|---|---|---|---|
| 2020-09-01 | 16:00 - 19:00 | Data analys | Herrman Rene | ||
| 2020-09-03 | 16:00 - 19:00 | Data analys | Herrman Rene | ||
| 2020-09-08 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-09-10 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-09-15 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-09-17 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-09-22 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-09-24 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-09-29 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-10-01 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-10-06 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-10-08 | 16:00 - 19:00 | D399 | Data analysis Theory exam | Herrman Rene | |
| 2020-10-13 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-10-15 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-10-20 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene | |
| 2020-10-22 | 16:00 - 19:00 | D399 | Data analys | Herrman Rene |