Basics

In this section we are going to discuss a lot of the mathematical basics in the form of Linear Algebra as well as some topics of statistics of sets. We will always immediately show how to use the discussed content in Python.

Note

These notes assume that you have some basic knowledge of programming in Python and we build on this knowledge. In this sense, we use Python as a tool and only describe the inner workings if it helps us to better understand the topics at hand.

If this is not the case have a look at MECH-M-DUAL-1-SWD, a class on software design in the same master program and from the same authors.

Additionally, we can recommend the following books on Python:

• Matthes (2023): Python Crash Course - A hands-on, project-based introduction to programming; Online Material.
• Python Cheat Sheet provided by Matthes (2023).
• McKinney (2022): Python for data analysis 3e; Online and Print
• Vasiliev (2022): Python for Data Science - A Hands-On Introduction
• Inden (2023): Python lernen – kurz & gut; German

Inden, Michael. 2023. Python Lernen - Kurz & Gut. Sebastopol: O’Reilly.

Matthes, Eric. 2023. Python Crash Course - a Hands-on, Project-Based Introduction to Programming. 3rd ed. No Starch Press. https://ehmatthes.github.io/pcc_3e/.

McKinney, Wes. 2022. Python for Data Analysis 3e. 3rd ed. Sebastopol, CA: O’Reilly Media. https://wesmckinney.com/book/.

Vasiliev, Yuli. 2022. Python for Data Science - a Hands-on Introduction. München: No Starch Press.