|
|
|
||
|
The objective of the course is to provide students with basic principles of using R and Python programming languages in geosciences. Students will learn and practice the most common principles of data assessment typical for geoscience applications based on selected exercises, such as working with large data sets (time series, spatial data), their basic statistical evaluation (correlation, regression, trends in time series, interpolation) and visualisation. The course is focused on 1) assessment and statistical analysis of time series, 2) regression models, 3) methods of quantification of spatial autocorrelation, 4) principal component analysis, 5) working with raster and remote sensing data, and 6) modelling selected environmental processes. A part of the course will also be a “coding club”, which enables students to discuss with lecturers their data and codes used for their final theses.
The course is intended mainly for master students of Physical geography and geoecology, Hydrology and hydrogeology, as well as for bachelor students of Physical geography and geoinformatics, Earth Sciences, and Geography and Cartography. Last update: Jeníček Michal, doc. RNDr., Ph.D. (23.07.2025)
|
|
||
Last update: Jeníček Michal, doc. RNDr., Ph.D. (30.01.2023)
|
|
||
Last update: Jeníček Michal, doc. RNDr., Ph.D. (06.02.2025)
|
|
||
Last update: Jeníček Michal, doc. RNDr., Ph.D. (06.02.2025)
|
|
||
Last update: Jeníček Michal, doc. RNDr., Ph.D. (10.09.2025)
|