| layout | root |
|---|---|
lesson |
. |
With Software Carpentry lessons and Data Carpentry lessons you learn the fundamental data skills needed to conduct research in your field and learn to write simple programs.
This one-day workshop will introduce you to Python for analyzing and visualizing spatial-temporal data. We will be using datasets from the environmental sciences that are freely available.
We will learn:
- how to identify some of the most common data formats (raster formats) in environmental Sciences i.e. netCDF and HDF (HDF-EOS and HDF5), GeoTIFF.
- How to view the content of these binary files
- How to identify the provenance and explore metadata
- How to organise your data and develop a simple data management plan to ease your work
- How to access and process data in commonly used raster formats such as GeoTIFF, HDF-EOS, HDF5 and netCDF.
- How to combine them with vector datasets such as ESRI Shapefile and GeoJSON.
The meaning of these terms will become clear as we work through the python notebooks.
Learners need to understand what files and directories are and what a working directory is. These concepts are covered in the Unix Shell lesson.
Learners need to have some prior knowledge of Python. For instance, what is covered in the Software Carpentry lesson Programming with Python is more than sufficient.
Learners must install Python and a few additional python libraries before the class starts. See [the setup instructions]({{ page.root }}/setup/)
Learners must get the metos data before class starts: please download and unzip the file metos-python-data.tar.
Please see [the setup instructions]({{ page.root }}/setup/) for details. {: .prereq}