Description
COURSE CONTENT:
Over the past few years, the Python language and the wider Python
ecosystem have become invaluable tools in research computing and data
analytics.
Python is (for the most part) an interpreted language and can be slow,
but depending on the application, there are ways to speed up Python code
and take advantage of multi-core and distributed computational
resources.
In this online hands-on workshop you will have the opportunity to
discover and gain experience with some tools and techniques that can be
used to improve the execution speed of your Python code.
The workshop will introduce a number of ways to measure the efficiency
of your code and improve it's speed of execution, including:
- Choosing suitable data structures, algorithms and libraries
- Profiling your code to discover what is slow or requires a lot of
memory
- Vectorisation
The content covered in this workshop will prepare you for exploring more
advanced and specialised optimization techniques such as parallelization
and GPUs which will be the focus of future workshops.
PREREQUISITES:
Attendees are expected to be familiar with concepts and techniques
covered in our Introductory Python Course, including familiarity with
Python syntax, running Python code, and awareness of the Python Standard
Library.
Experience with Python programming for data processing and awareness of
Python Decorators will be advantageous.
Familiarity with Google Colab will also be advantageous as we will use
this environment in the workshop. Please ensure you have created a
Google account prior to the workshop as this is needed to access the
Colab environment.
Duration: One Day (10-4pm)
Cost: Free of Charge
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