Python: Basics for Data Scientists
Current announcements
Nothing at the moment.
Course requirements
- a laptop or desktop computer (no specific requirements except an internet connection) with a working Anaconda ꜛ installation
- please download in advance the course material from the course’s GitHub repository:
- on the GitHub repository page, click on the green “Code” button and choose “Download Zip” (example)
- extract the Zip package and move the unpacked folder to your desired location on your hard drive (e.g., create a course folder in your documents folder)
- during the course, please visit this website to stay up to date (see Current announcements section).
Important note: Before the course starts, please make sure, that Anaconda is working on your device. We can not provide installation or technical assistance during the course.
Trouble shootings: If you have problems with your computer and/or Anaconda, you can use an online Python compiler, e.g., Google Colab ꜛ. Please, ensure before the beginning of the course, that you can access the online compiler of your choice (e.g., create a Google account) and that you know how to operate it (again, during the course we can not provide installation or technical assistance).
Syllabus
Chapter 1: Scientific programming languages
Chapter 2: Getting started with Anaconda and Spyder
Chapter 3: Jupyter Notebooks
Chapter 4: Variables
Chapter 5: Formatted printing
Chapter 6: Deep vs. shallow copy
Chapter 7: for-loops
Chapter 8: if-conditions
Chapter 9: Function definitions
Chapter 10: NumPy - Our data container
Chapter 11: Data visualization with Matplotlib
Chapter 12: Reading data with Pandas
Chapter 13: Statistical Analysis with Pingouin
Further Readings
Voluntary homework: After the first part of this course, i.e., after Chapter 9, feel free to solve this voluntary homework.
Info: Chapters 4 - 13 are available as Jupyter notebooks on , which can also be opened on
Follow-up
Don’t miss the Python Course: Neuro-Practical course, where you can apply your newly learned programming skills.
Past courses
- 2023, March: DZNE Workshop series (2.5 days)
- 2022, September: DZNE Workshop series (2.5 days)
- 2022, January: DZNE Workshop series (2.5 days)
- 2021, September: DZNE Workshop series (2 days)
- 2021, March: DZNE Workshop series (2 days)
- 2020-2021: Lab internal course series (weekly, closed)
- 2020, October: DZNE Workshop series (2 days)
- 2020, May: DZNE Workshop series (2 days)
This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).
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