This repository contains all of the code and data related to the Fall 2022 (E22) Python workshop for Cognitive Science at Aarhus University, Faculty of Arts. The workshop is taught by Center for Humanities Computing Aarhus, any inquiries can be addressed to CHCAA.
The workshop consists of a three-day introduction scientific computing with Python. It assumes some previous experience with programming but could in principle be completed with no prior experience. The course introduces Python data types and control flow, focusing on object-oriented programming and good scripting principles. It also introduces students to fundamental data science tools in Python, such as dataframes and plotting.
As the workshop is designed with CogSci students in mind, there is a particular focus on the use of Python in the mathematical contexts such as linear algebra. By the end of the workshop, students will be familiar with the key principles and techniques underlying scientific computing with Python.
This repository is in active development. Previous iterations of this workshop can be found by switching tags.
This repository has the following directory structure:
| Description | |
|---|---|
modules/ |
Contains all the teaching used in the workshop. |
resources.md |
Contains additional resources for the workshop. |
Within the modules/ directory, there are the following subdirectories:
| Module | Description |
|---|---|
01_introduction_and_types |
Introduction to Python and data types. |
02_control_flow |
Control flow and functions. |
03_oop |
Object-oriented programming. |
04_scripting |
Introduction to scripting. |
05_pandas_matplotlib |
Introduction to data science tools in Python. |
06_debugging |
Introduction to debbuging tools. |
07_numpy |
Introduction to mathematical computing with numpy. |
We will be going through the modules in order. However before that we will:
- Setup and introduce UCloud. However, feel free to install Python on your own computer if you prefer.
- Do a quick questionnarie to gauge your experience with Python.
- Introduce Jupyter Notebooks
- Get started with the first module
The general teaching approach will be a quick introduction to a module and then a self-paced work through of the module. The modules are designed to be self-contained and can be completed at your own pace. If you have any questions, feel free to ask the instructors.
- Fork it!
- Create your feature branch:
git checkout -b my-new-feature - Commit your changes:
git commit -am 'Add some feature' - Push to the branch:
git push origin my-new-feature - Submit a pull request
This project is licensed under the MIT License - see the LICENSE.md file for details.