548-0092/01 – Interpretation of geodata (IGD)
| Gurantor department | Department of Geoinformatics | Credits | 3 |
| Subject guarantor | doc. RNDr. Jan Caha, Ph.D. | Subject version guarantor | prof. Ing. Igor Ivan, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 2 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2015/2016 | Year of cancellation | 2022/2023 |
| Intended for the faculties | HGF | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
- Student demonstrates knowledge:
• basics of working with the program R
• selected statistical methods
- Students can:
• edit the data matrix
• apply selected statistical methods
• reduce the size of the input data matrix
- Student is able to:
• interpret the results
• extract the maximum information from the supplied data and geodata
Teaching methods
Tutorials
Summary
This course is focused on a practical work with data, its pre-processing, formatting and transformations. Practical introduction of basic statistical methods of data dimensions reduction and other multivariate methods such as factor, discriminant and cluster analysis and decision trees. Also basic principles of data mining are introduced. The aspect of results interpretation and maximal mining of information from data and geodata are key aspects of this course.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Získané znalosti studentů jsou průběžně ověřovány v průběhu jednotlivých hodin. Studenti také pracují na samostatných projektech, které prokazují získané znalosti.
E-learning
Other requirements
No additional requirements are imposed on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1) Methods of pre-processing of data and geodata for further processing
2) Errors in data and geodata and dealing with errors
3) Basics in R
4) R and geodata
5) Principal component analysis and interpretation of results
6) Factor analysis and interpretation of results
7) Discriminant analysis and interpretation of results
8) Cluster analysis and interpretation of results
9) Decision trees and interpretation of results
10)Basics of data mining
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction