https://github.com/eulerlab/python-course-2025
Information about the course "Basic Programming - Introduction into Python", summer term 2025
https://github.com/eulerlab/python-course-2025
Last synced: 5 months ago
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Information about the course "Basic Programming - Introduction into Python", summer term 2025
- Host: GitHub
- URL: https://github.com/eulerlab/python-course-2025
- Owner: eulerlab
- License: mit
- Created: 2025-04-09T08:22:39.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-22T10:06:43.000Z (about 1 year ago)
- Last Synced: 2025-06-22T11:19:17.311Z (about 1 year ago)
- Size: 89.8 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Python Course 2025
This repository contains information about the course _"Basic Programming - Introduction into Python"_ (2025).
## Acknowledgments
In this class, we will follow largely (but not exclusively) the course [NESC 3505 Neural Data Science](https://neuraldatascience.io/intro.html), developed at Dalhousie University as an open educational resource.
## Course structure
### Approach
This course follows an inverted classroom approach, which means you prepare the material for the sessions at home, leaving the actual sessions for discussions, questions, and problem-solving.
### Schedule
### Materials
The materials consist of
- Online chapters, which will provide you with the respective background
- Jupyter notebooks, in which you can learn and practice Python concepts
- YouTube videos, which go through the notebooks step-by-step. We highly recommand to try to do the notebooks first by yourself, and only use the videos if you encounter major difficulties
__Before every session__, you need to read a few chapters and do the respective Jupyter notebooks. The notebooks are divided into a lesson part, where the concepts are introduced and demonstrated, and an exercise part, where you can apply the knowledge just gained. The latter exercise notebooks (name starts with `x_`, e.g. `x_for-loops.ipynb`) need to be submitted to the `Exercise` folder in ILIAS, following the instructions below.
__Submission guidelines__: Put all exercise-noteboks in a *single* zip-file. The name of the zip-file should start with the number of the exercise (e.g. 1a or 2b) and should end with your last name (e.g. 2a-Euler). Do not submit the lecture notebooks, i.e. submit `x_for-loops.ipynb` but not `for-loops.ipynb`.
__During the sessions__, we will discuss what you learned, where you encountered problems, and how to solve these.
> _Important: The links to chapters point at the original class material, whereas the notebooks you will find in your `bwJupyter` environment - as demonstrated in the first session._
### Important links
[Link to bwJupyter environment](https://hub.bwjupyter.de/services/profilemanagement/add?profile=cc0f3dc4-ec2d-42ac-9b5e-84a67ccc915c)
[Link to Zoom room for screen sharing](https://med-uni-tuebingen-de.zoom-x.de/j/61228841347?pwd=baExSBbdq2wUt1U4bBlQt6DbTshsxI.1)
## 25.4. | Introduction, Setup, Project overview
__To prepare before:__
- Read chapters ["About This Course"](https://neuraldatascience.io/1-intro/why.html) (all sections) and ["Introduction to Data Science"](https://neuraldatascience.io/2-nds/introduction.html) (all sections)
__During the class:__
- _Why this course?_ About adult learners and your motivation to learn Python, your programming/Python background, that the only way to learn to code is to write it, the importance of coding skills for science and beyond, and the use of AI tools.
- _The organisation of this course._ Time budget outside the classroom, videos as the last resort, exercises and final project.
- _Setting up `bwJupyter.de`__ and accessing the curse material. How to submit exercises.
- _Getting started_ with the chapters for the following class.
## 02.05. | Variables & Assignment, Data Types & Conversion, Python Built-ins, Lists, Dictionaries
__To prepare before:__
- Read chapter ["Introducing Python"](https://neuraldatascience.io/3-python/introduction.html); you can ignore the section `Deactivate AI for Now`. Also, read the next chapter with the respective learning objectives.
- On bwJupyter: do the exercises in `1-Introducing-Python\1a` and submit the exercises to ILIAS.
## 09.05. | For loops, Conditionals, pandas, Looping over datafiles
__To prepare before:__
- On bwJupyter: do the exercises in `1-Introducing-Python\1b` and submit the exercises to ILIAS.
## 16.05. | Visualisation with Matplotlib, Procedural versus Object-Oriented Plotting in Matplotlib, Subplots
__To prepare before:__
- Read chapter ["Introduction to Data Visualization"](https://neuraldatascience.io/4-viz/introduction.html) and the respective learning objectives.
- On bwJupyter: do the exercises in `2-Visualizing-Data\2a` and submit the exercises to ILIAS.
## 23.05. | DataTypes, Seaborn, Human Factors
__To prepare before:__
- On bwJupyter: do the exercises in `2-Visualizing-Data\2b` and submit the exercises to ILIAS.
## 30.05. | AI & IDEs
__To prepare before:__
- nothing
__Lecture:__
- A demonstration of modern AI assistants for coding.
- A demonstration of modern [IDEs](https://en.wikipedia.org/wiki/Integrated_development_environment) that make coding, debugging and version control much easier.
The lecture presentation was uploaded to Ilias.
## 06.06. | Intro to EDA & Repeated Measures Data, Data Cleaning and Outliers
__To prepare before:__
- Read chapter ["Working with Repeated Measures Data"](https://neuraldatascience.io/5-eda/repeated_measures.html)
- Read chapter ["Data Cleaning - Dealing with Outliers"](https://neuraldatascience.io/5-eda/data_cleaning.html)
- On bwJupyter: do the exercises in `3-EDA\3a` and submit the exercises to ILIAS.
## 20.06. | Numpy, Tests (Scipy)
__To prepare before:__
- Read chapter ["Basic Statistics in Python: t tests with SciPy"](https://neuraldatascience.io/5-eda/ttests.html)
- On bwJupyter: do the exercise in `3-EDA\3b` and submit the exercise to ILIAS.
## 27.06. | GitHub
__To prepare before:__
- Read the following short chapters on GitHub: ["Clone a Repository"](https://neuraldatascience.io/2b-setup/clone.html), ["Exploring the GitHub Repository view"](https://neuraldatascience.io/2b-setup/github_repo.html), ["Editing, Pushing, and Committing"](https://neuraldatascience.io/2b-setup/edit_commit_push.html), and ["Edit the README File"](https://neuraldatascience.io/2b-setup/edit_readme.html)
- No exercises to prepare
## 04.07. | Data Science Project 1
__To prepare before:__
- Create a GitHub repository and submit a file with the link. If you already use GitHub, you don't have to create a new repository, just submit a file with the link to your most complete repository.
- Read the chapters ["Introduction to Single Unit Data"](https://neuraldatascience.io/6-single_unit/introduction.html), ["Learning Objectives"](https://neuraldatascience.io/6-single_unit/learning_objectives.html), and all sections in ["Single Unit Data and Spike Trains"](https://neuraldatascience.io/6-single_unit/single_unit_intro.html)
## 11.07. | Data Science Project 1
Coming soon
## 18.07. | Data Science Project 2
Coming soon
## 25.07. | Data Science Project 2
Coming soon