Welcome!

Warning

These pages are currently under construction and will be continuously updated. Please check back often, especially as new sections will be added during the semester.

Hello, and welcome to the course page for “Python for Psychologists”, part of the Psychology Master’s program at Goethe University Frankfurt for the Winter Term 2024.

This platform will serve as your guide throughout the course, providing you with essential information such as formal requirements, lecture materials, practical assignments, and much more. This resource is built using Jupyter Book, which allows us to integrate interactive code and tutorials directly into the course material.

You can navigate through the respective sections via the TOC (table of contents) on the left side and within sections via the TOC on the right side. The three symbols in the top allow enabling full screen mode, link to the underlying Github repository and allow you to download the contents as a pdf or jupyter notebook respectively. Some sections will additionally have a little rocket in that row which will allow you to interactively rerun certain parts of the practicals via cloud computing. All of this awesomeness (talking about the infrastructure and resource) is possible through the dedicated and second to none work of the Jupyter community, specifically, the Executable/Jupyter Book and mybinder project.

Python for Psychologists

This course is designed to introduce psychology students to the world of programming, focusing on its application to psychological research. While programming might seem intimidating at first, it is an incredibly powerful tool for data acquisition, analysis, and even experiment design.

Within this course we will explore the Python programming language, specifically how it can and why it should be utilized within experimental psychology. To do so, we will follow a “learning by doing” approach in a tripartite manner. Starting from a basic introduction into programming and python (Block I), we will evaluate how python can be used to run experiments (Block II) and analyze the resulting data (Block III). Thus, we actively seek out realistic examples and workflows, trying to solve problems with python. Along this way we will also talk about important adjacent topics such as computing environments and IDEs. For a more precise outline of the course, please consult the respective section. This course is designed to provide lecture content in a way that it is FAIR for as many people as possible.

You can use the following sections to navigate through the content of the lecture:

• Overview & procedure

  What’s this course all about? How are things implemented and supposed to work?

• General outline

  What are the specific topics and aspects taught?

• Introduction

  All things gotta start somewhere and using programming within research settings is no exception to that, but how?

• Running experiments in Python (soon)

  How can you use python to conduct experiments to acquire data from participants?

• Data analyses in Python (soon)

  How can you use python to obtain insights from data, including preprocessing, statistics and visualizations?

• ChatGPT and others

  The rules for using AI.

• Code of Conduct

  Necessities for creating an open, fair, safe and inclusive learning experience.

I’ve got a question!

In case you have any questions or difficulties with the lecture and its materials, please don’t hesitate a single second to get in touch with us. A great way to do this is to open an issue on the GitHub site of the course. You can of course further contact me via E-mail. Every feedback or idea you might have is highly appreciated and valued.

Acknowledgements

This course was initially composed and designed by Peer Herholz and adapted by Aylin Kallmayer and teached to you this winter by Yury Markov.

Peer Herholz’ work on and ability to compile this course was enabled through training received at the Montreal Neurological Institute, specifically the NeuroDataScience - ORIGAMI lab supported by funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative, the National Institutes of Health (NIH) NIH-NIBIB P41 EB019936 (ReproNim), the National Institute Of Mental Health of the NIH under Award Number R01MH096906 (Neurosynth), a research scholar award from Brain Canada, in partnership with Health Canada, for the Canadian Open Neuroscience Platform initiative, as well as an Excellence Scholarship from Unifying Neuroscience and Artificial Intelligence - Québec.