Scientific Python

Content

The aim of this course is to introduce the audience to the ecosystem of scientific Python. The course will enable participants to efficiently use Python libraries as a part of their scientific workflow. Introductory presentations are accompanied by small practical demonstrations and take-home exercises.

The following topics will be covered:

• Introduction to the scientific Python stack
• basic overview of NumPy & Pandas
• selected SciPy methods

The basis of the scientific Python stack presented in this course consists of Anaconda and Jupyter. Anaconda environments enable the encapsulation of projects and their dependencies. Jupyter Notebooks make for a convenient front-end for data analysis and code prototyping. 

Numpy introduces the concept of array oriented programming, which helps to quickly operate on array-like datasets in only a few lines of code. It combines the speed and efficiency of the underlying compiled code with Python's ease of use. 

Pandas is a tool for efficiently handling large sets of tabulated data, for example for clustering, aggregating, and visualizing. It heavily relies on NumPy but additionally includes semantic information. 

Scipy is a powerful library that features most of the tools needed in the context of scientific work: linear algebra, FFT, numerical optimization and many more.

Agenda

• 09:00 - 12:00 Morning session - Introduction to Anaconda and Jupyter notebooks, Numpy
• 12:00 - 13:00 Lunch break
• 13:00 - 17:00 Afternoon session - Pandas, Scipy and Examples

Trainer(s)

• Marcel Giar (HKHLR)
• Philipp Risius (JLU Gießen)

Participation

• Participants are expected to have knowledge of basic Python syntax and constructs.
• Experience in creating and using environments with Anaconda or Python 'venv' is helpful.
• Basic concepts of linear algebra (matrices, vectors, transformations) will be used, but proficiency is not required.