Scientific computing with Python and NumPy
Course further information
Python has become one of the most popular programming languages not least due to its ease of use. However, the language features that provide that ease of use also limit its computational performance. But there are tools to accelerate Python, even to the degree that it has entered the domain of high-performance computing.
One of those tools is the NumPy package. NumPy provides Python with an efficient array datatype and accompanying compute functions which together form the foundation of many of todays scientific libraries.
In this workshop, you are going to learn how use NumPy to solve your own computing tasks. We start by discussing what makes Python slow compared to other languages and how NumPy arrays remedy the situation. We are going to look at NumPy’s memory model, introduce you to the most useful functions of the package, and show how you can use NumPy for tasks from element-wise array operations, over linear algebra, to the implementatin of numerical methods.
To foster an interactive atmosphere among participants and instructors, this workshop is offered in person and not as a hybrid course.
The course language is English.
Prerequisites
To take part in this workshop, you should be familiar with the basics of Python.
We encourage you to bring your own laptop. All you need is a working Python installation with JupyterLab or Jupyter Notebook installed.
