Today, Python is the programming language most commonly used by Geospatial professionals. Developing programming skills allows you to further interact with a range of commercial and open source GIS software. It will also allow you to build standalone scripts that will enable the automation of workflows and bring data processing to scale in the cloud. Being able to program will make a positive impact your ability to further your exploitation and utilisation of Earth Observation data.
I have written code for numerous clients and employeers over my career, I want to share this knowledge and experience with you. Some examples of my Python code can be found here.
I offer two courses
These can be customised to meet your organisations requirements delivered at your office. However, when there is interest I do run group training offsite. If you are interested in this please contact me on firstname.lastname@example.org.
A general guide to each course content is given below.
1. Aimed at Beginners Fundamentals of Python
- Advanced lists – Challenge 1
- Other data types
- operators review – Challenge 2
- errors – Challenge 3
- function – Challenge 4 (gets harder)
- conditional statement – Challenge 5
- loops – Challenge 6
- misc, installing (light)
- Working with directories – challenge 7 (harder)
- Working with textfiles – Challenge 8 (harder)
- Reading a shapefile
- Reading Raster data
- Graphing data – Challenge 9
- Command line gdal
- Raster Boundary (making steps to intermediate level)
- Fastest image (there is a blog on this), but it is to introduce wider ideas
- Challenge 10
- Learn Python 3 Basics.
- We are aiming to build simple scripts that can be used in a Geospatial context.
- Read and write shapefiles / use GDAL to convert a raster into an array
- Write functions – begin building up a set of scripts that you can reuse.
- To enjoy and appreciate the value Python can have within your working day and subseqently your career.
Find the joing instructions here:
2. Aimed at Intermediate users (min 1-2 years)
- GDAL and numpy working with arrays + rasterio
- Challenge 1 – GDAL
- Computer vision display?
- Computer Vision 1 – intro, interactive images
- Challenge 2 – Computer Vision (combined with directories)
- Computer Vision – Analytics
- Computer Vision – Analytics prt2
- Challenge 3 – Analytics
- Sklearn – pixels and shapefiles
- Challenge 4 – Sklearn
- Sklearn – Machine learning, filtering on pixels, metric reporting
- Challenge 5 – thinking about different classifications
- PostGIS – connection, loading, querying
- Challenge 6 – PostGIS
— Bonus —
small segements on raster stats, pandas and geopandas
Contact me email@example.com
Both courses are built using Jupyter Notebooks, though the course can be given using other editors. Attendees get to keep the code, no one gets left behind.
I have learnt Python over several years by making mistakes, asking questions and practice. This is the way these courses are run, overload can be a killer especially for beginners and by working together we can customise a learning pathway for attendees.
Visit https://github.com/acgeospatial/Geospatial_Course_Example for notebook