| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction |
What are some of the common computing terms that I might encounter?
How can I use Python to work with large datasets? How do I connect to a high performance computing system to run my code? |
| 00:35 | 2. Coffee Break | Break |
| 00:50 | 3. Dataset Parallelism | How do we apply the same command to every file or parameter in a dataset? |
| 01:15 | 4. Lunch Break | Break |
| 02:15 | 5. Parallelisation with Numpy and Numba |
How can we measure the performance of our code?
How can we improve performance by using Numpy array operations instead of loops? How can we improve performance by using Numba? |
| 03:35 | 6. Coffee Break | Break |
| 03:50 | 7. Working with data in Xarray |
How do I load data with Xarray?
How does Xarray index data? How do I apply operations to the whole or part of an array? How do I work with time series data in Xarray? How do I visualise data from Xarray? |
| 05:20 | 8. Plotting Geospatial Data with Cartopy | How do I plot data on a map using Cartopy? |
| 06:10 | 9. Coffee Break | Break |
| 06:25 | 10. Parallelising with Dask |
How do we setup and monitor a Dask cluster?
How do we parallelise Python code with Dask? How do we use Dask with Xarray? |
| 07:45 | 11. Lunch Break | Break |
| 08:45 | 12. Storing and Accessing Data in Parallelism Friendly Formats |
How can we use an object store to store data that is accessible over the internet?
How do we access data in an object store using Xarray? |
| 10:05 | 13. Coffee Break | Break |
| 10:20 | 14. GPUs |
What are GPUs and how do we access them?
How can we use a GPU with Numba? How can we use a GPU in Pandas, Numpy or SciKit Learn? |
| 11:20 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.