Spatial data analysis with Python

This course teaches you how to do different GIS-related tasks in Python programming language. Each lesson is a tutorial with specific topic(s) where the aim is to learn how to solve common GIS-related problems and tasks using Python tools. In the lessons we use only publicly available data which can be used and downloaded by anyone anywhere. The course is based on Helsinki University's course Automating GIS processes. We will be using open-source Python packages, not ArcPy (used in ArcGIS). 

Topics of the course

  • GIS in Python; Spatial Data Model, Geometric Objects, Shapely
  • Working with (Geo)DataFrames
  • Geocoding and spatial queries
  • Geometric operations, reclassifying data
  • Visualization, static and interactive maps
  • Raster data processing in Python
  • Running Python scripts on CSC's Puhti supercomputer

Practical machine learning with spatial data

This course gives a practical introduction to machine learning with spatial data, both to shallow learning and deep learning models, including convolutional neural networks (CNN). The course consists of lectures and hands-on exercises in Python. We will use scikit-learn for the shallow learning exercises and keras for deep learning exercises.

After the course the participants should have the skills and knowledge needed to start applying machine learning for different spatial data analysis tasks. In addition, participants will be able to makes use of the GPU resources available at CSC High Performance Computers for training and deploying their own machine learning models.

Spatial data analysis with R

The aim of the course is to familiarize participants with spatial analysis with R.

Topics of the course:

  •     Handling and plotting vector data in R
  •     Spatial operations (intersection, clipping, conversions etc)
  •     Spatial analysis of vector data (clustering, density surfaces, autocorrelation)
  •     Visualizing spatial data
  •     Raster basics with R
  •     Raster data manipulation
  •     Map algebra
  •     Spatial modelling with raster data

Geocomputing on the supercomputer

  • Are you working with geospatial data and running close to the limits of your own computing environment? 
  • Are you curious on how you can take your geospatial data processing and analysis to the next level? 
  • Or maybe you have been using a supercomputer already, but would like to make sure your are getting the most out of it?

    → This course is for you!

In this course we will learn the basics of geocomputing on a supercomputer through a combination of lectures and hands-on activities. The main focus of the course is Puhti supercomputer, were all hands-on exercises will be done. The CSC services discussed in this course are free-of-charge for academic research, education and training purposes for Finnish higher education institutions and state research institutes (subsidized by the Ministry of Education and Culture, Finland). 

Most of the course content also applies to LUMI supercomputer, which is available for academic users and companies. 

The course is meant both for academic researchers planning to use Puhti supercomputer and for data analysts from private companies planning to use LUMI.

If still unsure, if supercomputers could benefit you, see CSC geocomputing page.