Analysing and Visualising Measurement Data (5 cr)
Code: IE10033-3001
General information
- Enrollment
- 01.04.2024 - 30.04.2024
- Registration for the implementation has ended.
- Timing
- 26.08.2024 - 20.12.2024
- Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Insinöörikoulutus / Energia- ja ympäristötekniikka (IE)
- Campus
- Wärtsilä Campus Karjalankatu 3
- Teaching languages
- Finnish
- Seats
- 30 - 50
- Degree programmes
- Degree Programme in Energy and Environmental Engineering
- Teachers
- Tiina Soininen
- Jani Kangas
- Anniina Kontiokorpi
- Teacher in charge
- Anniina Kontiokorpi
- Groups
-
IENS22Engineer, Energy and Environmental Engineering, Full-Time Studies, Fall, 2022
- Course
- IE10033
Evaluation scale
Approved/Rejected
Objective
You know the concept and basic methods of data filtering and you are able to implement e.g. median and mean filtering, hypothesis testing and linear regression. You know the saving formats for measurement data and you are able to edit data to an appropriate format. You can follow the statistical change using the control card. You can present the results of the data you have analysed and draw conclusions from the results. You are able to visualize and present the key results of the analysed data relevant for the situation and the target group. You are familiar with the concept of knowledge management and you understand the importance of information as a part of a decision making process. You will familiarize yourself with defining the need for data, the concepts of knowledge management and knowledge management as a part of the organisation.
Content
- The amount and quality of the collected data
- Formats for saving data
- Data filtering
- Data analysis as a part of the entire data processing process
- Data editing
- Hypothesis testing
- Control card
- Linear regression
- Data visualisation and presentation
- Presentation of the analysed results
- Data needs
- Concepts of knowledge management
- Knowledge management as a part of the organisation
Location and time
Lähiopetuksena Wärtsilä-kampuksella.
Teaching methods
Opintojaksolla perehdytään ympäristötiedon analysointiin (datan suodatus, hypoteesin testaus, lineaarinen regressio jne.). Aineistoa muokataan ja visualisoidaan tarkoituksenmukaiseen muotoon minitab-ohjelmistolla ja power-bi-ohjelmistolla. Opintojaksolla opit seuraamaan tilastollista muutosta ohjauskortin avulla. Opit visualisoimaan ja esittämään analysoidun datan keskeiset tulokset tilanteeseen ja kohderyhmään sopivalla tavalla. Perehdyt tietotarpeen määrittelyyn, tiedolla johtamisen käsitteisiin ja tiedolla johtamiseen osana organisaatiota.
Datan analysointi (Jani Kangas).
Datan visualisointi, tiedolla johtaminen (Tiina Soininen).
Student workload
Opintojakson laajuus on 5 op, joka vastaa keskimäärin 135 tunnin kuormitusta opiskelijalle.