GIS self-study materials

GIS introductions and vocabulary

More learning materials can be found in GeoPortti training materials.

Software specific learning materials can be found from CSC software pages.


Materials of CSC GIS events

Geocomputing in CSC computing environment learning materials

Geocomputing using CSC resources course

Kylli Ek, Samantha Wittke 2021, 2022, 2023

Introduction to geocomputing in Puhti: using R, Python, GDAL. 

Geocomputing webinars

Geocomputing seminars

Geocomputing seminars. Short presentations of research projects where CSC geocomputing resources have been used.


Software or application specific courses

Introduction to Python GIS

Kamyar Hasanzadeh / HY, Samantha Wittke / CSC, 3-days course, 2024.

GIS in Python; Spatial Data Model, Geometric Objects, Shapely, working with (Geo)DataFrames, geocoding and spatial queries, geometric operations, reclassifying data, plotting and interactive maps, raster data processing in Python.


Spatial data analysis with R

Marko Kallio / Aalto university, 3-days course, 2023.


Practical machine learning for spatial data

Mats Sjöberg, Markus Koskela, Samantha Wittke, Kylli Ek, 2-days course, 2022.


STAC - how to find and use spatiotemporal data easily?

Kylli Ek, half-day workshop, 2023



of Lidar data analysis in Taito, with PDAL and R

Web GIS Enabled Spatial Analysis & Data Science with ArcGIS

Aki Kaapro / Esri Finland, 1-day course, 2021

Introduction to the spatial analysis framework within the ArcGIS platform for vector, point cloud, and raster data. Using Esri's ArcGIS Pro desktop app, spatial data science methods are applied for pattern detection and clustering, but also to make spatial data-based predictions and geoAI. How to use modern Web GIS implementation pattern, the new paradigm of how people can share, find, and use geographic information via a geospatial cloud.


PyQGIS: expanding QGIS’s functionality with Python

Tatu Leppämäki / University of Helsinki, 1-day course, 2021

A practical introduction to PyQGIS, the Python implementation in QGIS: running Python code through the built-in console, creating scripts, automating processes with the graphical Model Builder and processing tools, and creating a simple graphical QGIS plugin.

Introduction to using Google Earth Engine

Ulpu Leinonen / UTU, 2-days course, 2019.

Google Earth Engine (GEE) is an online platform which allows its users to find, process, analyze, and download satellite imagery and other Earth observation data using Google's infrastructure. The course topics: data types, code editor, accessing satellite imagery, calculations with data, working with vector data, compositing and mosaicking, image classification, time series.


Lidar data analysis in Taito, with PDAL and R

Elias Annila, Eduardo Gonzalez, Kylli Ek / CSC, 1-day course, 2019

The main tools covered in the course are: PDAL and different R packages, inc lidR and rlas. The objective is to get a general overview of tasks that can be done using these tools: filtering points, calculating digital elevation and surface models, calculating canopy height, tree detection, mesh creation, change detection.


Introduction to aerial LiDAR data management

Ville Kankare / HY, 1-day course, 2018.

Basic characteristics of LiDAR datasets and how to manage aerial LiDAR datasets using LasTools and R. Predicting forest attributes using area based approach and calculated metrics with R.