{"id":27441285,"url":"https://github.com/eulerlab/python-course-2025","last_synced_at":"2026-01-22T05:02:42.708Z","repository":{"id":286971554,"uuid":"963146690","full_name":"eulerlab/python-course-2025","owner":"eulerlab","description":"Information about the course \"Basic Programming - Introduction into Python\", summer term 2025","archived":false,"fork":false,"pushed_at":"2025-06-22T10:06:43.000Z","size":92,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-06-22T11:19:17.311Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/eulerlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-09T08:22:39.000Z","updated_at":"2025-06-22T10:06:47.000Z","dependencies_parsed_at":"2025-04-14T23:14:22.314Z","dependency_job_id":"17628413-a6b3-483d-b596-cad21faac1ef","html_url":"https://github.com/eulerlab/python-course-2025","commit_stats":null,"previous_names":["eulerlab/python-course-2025"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/eulerlab/python-course-2025","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eulerlab%2Fpython-course-2025","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eulerlab%2Fpython-course-2025/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eulerlab%2Fpython-course-2025/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eulerlab%2Fpython-course-2025/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eulerlab","download_url":"https://codeload.github.com/eulerlab/python-course-2025/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eulerlab%2Fpython-course-2025/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28655029,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-22T01:17:37.254Z","status":"online","status_checked_at":"2026-01-22T02:00:07.137Z","response_time":144,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-04-14T23:14:20.977Z","updated_at":"2026-01-22T05:02:42.696Z","avatar_url":"https://github.com/eulerlab.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Python Course 2025\nThis repository contains information about the course _\"Basic Programming - Introduction into Python\"_ (2025).  \n\n## Acknowledgments\nIn 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.\n\n## Course structure\n### Approach\nThis 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.\n\n### Schedule\n \u003cimg src=\"https://github.com/eulerlab/python-course-2025/blob/main/python-course-2025-schedule_v2.png\" alt=\"alt text\" width=\"600\"/\u003e\n\n### Materials\nThe materials consist of\n- Online chapters, which will provide you with the respective background\n- Jupyter notebooks, in which you can learn and practice Python concepts\n- 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\n\n__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.\n\n__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`.\n\n__During the sessions__, we will discuss what you learned, where you encountered problems, and how to solve these.\n\n\u003e _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._\n\n### Important links\n[Link to bwJupyter environment](https://hub.bwjupyter.de/services/profilemanagement/add?profile=cc0f3dc4-ec2d-42ac-9b5e-84a67ccc915c)  \n[Link to Zoom room for screen sharing](https://med-uni-tuebingen-de.zoom-x.de/j/61228841347?pwd=baExSBbdq2wUt1U4bBlQt6DbTshsxI.1)\n\n## 25.4. | Introduction, Setup, Project overview\n__To prepare before:__\n- 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)\n\n__During the class:__\n- _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.\n- _The organisation of this course._ Time budget outside the classroom, videos as the last resort, exercises and final project.\n- _Setting up `bwJupyter.de`__ and accessing the curse material. How to submit exercises.\n- _Getting started_ with the chapters for the following class.\n\n## 02.05. | Variables \u0026 Assignment, Data Types \u0026 Conversion, Python Built-ins, Lists, Dictionaries\n__To prepare before:__\n- 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.\n- On bwJupyter: do the exercises in `1-Introducing-Python\\1a` and submit the exercises to ILIAS.\n\n## 09.05. | For loops, Conditionals, pandas, Looping over datafiles\n__To prepare before:__\n- On bwJupyter: do the exercises in `1-Introducing-Python\\1b` and submit the exercises to ILIAS.\n\n## 16.05. | Visualisation with Matplotlib, Procedural versus Object-Oriented Plotting in Matplotlib, Subplots\n__To prepare before:__\n- Read chapter [\"Introduction to Data Visualization\"](https://neuraldatascience.io/4-viz/introduction.html) and the respective learning objectives.\n- On bwJupyter: do the exercises in `2-Visualizing-Data\\2a` and submit the exercises to ILIAS.\n\n## 23.05. | DataTypes, Seaborn, Human Factors\n__To prepare before:__\n- On bwJupyter: do the exercises in `2-Visualizing-Data\\2b` and submit the exercises to ILIAS.\n\n## 30.05. | AI \u0026 IDEs \n__To prepare before:__\n- nothing\n\n__Lecture:__\n- A demonstration of modern AI assistants for coding.\n- A demonstration of modern [IDEs](https://en.wikipedia.org/wiki/Integrated_development_environment) that make coding, debugging and version control much easier.\n\nThe lecture presentation was uploaded to Ilias.\n\n## 06.06. | Intro to EDA \u0026 Repeated Measures Data, Data Cleaning and Outliers\n__To prepare before:__\n- Read chapter [\"Working with Repeated Measures Data\"](https://neuraldatascience.io/5-eda/repeated_measures.html)\n- Read chapter [\"Data Cleaning - Dealing with Outliers\"](https://neuraldatascience.io/5-eda/data_cleaning.html)\n- On bwJupyter: do the exercises in `3-EDA\\3a` and submit the exercises to ILIAS.\n\n## 20.06. | Numpy, Tests (Scipy)\n__To prepare before:__\n- Read chapter [\"Basic Statistics in Python: t tests with SciPy\"](https://neuraldatascience.io/5-eda/ttests.html)\n- On bwJupyter: do the exercise in `3-EDA\\3b` and submit the exercise to ILIAS.\n\n## 27.06. | GitHub\n__To prepare before:__\n- 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)\n- No exercises to prepare\n\n## 04.07. | Data Science Project 1\n__To prepare before:__\n- 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.\n- 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)\n\n## 11.07. | Data Science Project 1\nComing soon\n\n## 18.07. | Data Science Project 2\nComing soon\n\n## 25.07. | Data Science Project 2\nComing soon\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feulerlab%2Fpython-course-2025","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feulerlab%2Fpython-course-2025","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feulerlab%2Fpython-course-2025/lists"}