548-0092/03 – Interpretation of geodata (IGD)
| Gurantor department | Department of Geoinformatics | Credits | 3 |
| Subject guarantor | doc. RNDr. Jan Caha, Ph.D. | Subject version guarantor | doc. RNDr. Jan Caha, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 2 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2021/2022 | Year of cancellation | |
| Intended for the faculties | HGF, EKF | 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. Na základě jejich úspěšného odevzdání a přijetí je studentům udělen zápočet.
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) Basics in IBM SPSS Statistics
5) R and geodata
6) Principal component analysis and interpretation of results
7) Interpretation of principal component analysis results
8) Factor analysis, interpretation of results
9) Cluster analysis
10) Interpretation of cluster analysis results
11) Decision trees
12) Interpretation of decision tree results
13) Basics of data mining
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction