About this course
The Python programming language is popular in scientific computing because of the benefits it offers for fast code-development. The performance of pure Python programs is often suboptimal, but there are ways to make them faster and more efficient.On this course, you’ll find out how to identify performance bottlenecks, perform numerical computations efficiently and extend Python with compiled code. You’ll learn various ways to optimise and parallelise Python programs, particularly in the context of scientific and high-performance computing. Free to register. Fee to certificate.
- Improve performance of Python programs.
- Apply parallelisation techniques using Python.
- Calculate numerical expressions efficiently in Python.
- Identify performance bottlenecks in Python programs.
- Develop higher-performance extensions to Python.