This repository holds teaching materials for the NCAS Introduction to Scientific Computing course.
The course covers:
- Introduction to the Linux shell
- Python Setup
- Git and GitHub
- Introduction to Python
- Data manipulation and visualisation in Python (Working with Data)
- Example code for all python modules
- Algorithmic thinking
- Introduction to Net Zero DRI
- Why good data curation is essential to doing good science
- Running and Quitting
- Variables and Assignment
- Data Types and Type Conversion
- Built-in Functions and Help
- Libraries
- Reading Tabular Data into DataFrames
- Pandas DataFrames
- Plotting
- Lists
- For Loops
- Conditionals
- Looping Over Data Sets
- Writing Functions
- Variable Scope
- Programming Style
- Data formats and metadata - why?
- Text formats
- Some more common text formats (at CEDA)
- Binary formats
- Overview of NetCDF
- The structure of "Classic" NetCDF files
ncgen
andncdump
to create/export NetCDF and CDL- The CF Metadata Conventions (for NetCDF)
- Checking CF-compliance:
cf-checker
- Reading NetCDF files with Python:
netCDF4
- Creating NetCDF files with Python
- Reading and writing other formats
- Viewing NetCDF:
Ncview
andncBrowse
See the Resources page for links to useful related sites and materials.
Feel free to fork this repository on GitHub and re-use these materials however you like.
The foundations of our course are based on the superb materials provided by Software Carpentry who we are eternally grateful to.