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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.

Register now for the first run starting on 9th September 2019 at
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