External resources

Resources used (and linked) in these lecture notes:

Prerequisites:

• Programming for Everybody (Getting Started with Python) from EdX: if you have no programming experience, this is recommended before you take this lecture, this will guide you through the first programming steps. If you are able to learn by yourself, the textbook is available for free.

Linux and bash scripting:

• Ryan’s Tutorials for the linux command line and bash scripting are entertaining and well designed.

Python tutorials:

• the official tutorial, always a good place to start.

• the scipy lecture notes, a good overview of the scientific python stack for scientists with previous programming experience.

Python reference:

• the python documentation is your best reference for any question related to the language and the standard library.

Testing

• Katy Huff’s scientific python testing tutorial: a one and a half hour introduction to testing practices, from the basics to continuous integration

Floating point precision errors

• Python’s Floating Point Arithmetic article is a good read but a bit tough for beginners

• What Every Programmer Should Know About Floating-Point Arithmetic is a more gentle introduction, but you still might want to use a search engine for more information.

Numpy

• Why python is slow by Jake Vanderplas

• From Python to Numpy by Nicolas P. Rougier: an open-access book on numpy vectorization techniques (rather advanced).

• The numpy reference: the official documentation is always the best place to obtain first hand information.

Scientific Python

• Python Data Science Handbook by Jake Vanderplas: an open-access textbook on using Python for Scientists

Python scripts, packages, namespaces and scopes

• an entry level tutorial from RealPython about python scripts

• blog post by Sebastian Raschka: A Beginner’s Guide to Python’s Namespaces, Scope Resolution, and the LEGB Rule

Object Oriented Programming The web is full of blog posts and basic tutorials about OOP in python. Unfortunately, most of them make a poor job at explaining why OOP can be useful and when not. I will try to find better resources, but for now I recommend:

• RealPython’s beginner tutorial

• Wikipedia page about OOP

• Wikipedia page about inheritance

Documentation

• A beginner’s guide to writing documentation

Some youtube videos about tech in general

• A fun but though-provoking talk about machine learning and why we should think before we do things

(this list will be updated when the notes get written further)