Please note that this is not a beginner's course. Please answer the questions in the registration link to help the trainer assess your level better. If your level is different than the one required in this course, please look at our website (under Software and Programming Skills) for other courses, or email us directly. Please also note this workshop is intended for postdocs, and preference will be given to postdocs while registering.
This is a blended learning workshop for Data Analysis using Python.
Course Structure
The following four Self-learning Modules will be provided to you as online videos well in advance to help you better prepare for the workshop:
- Self-learning Module 1: Introduction, Virtual Environments, Jupyter, Notebook extensions, Python Fundamentals
- Self-learning Module 2: Syntax, PEP8, Keyboard Shortcuts, First examples in Numpy and Matplotlib
- Self-learning Module 3: Advanced Numpy, File IO, Pandas, ChatGPT
- Self-learning Module 4: Advanced Matplotlib, Plotting options, Inset Plots, Contour Plots, Interactive Plots
Live Tutorial Sessions:
- Day 1 (Friday, 18.10, 9-11 AM): Discussion of Self-learning Modules (1-4)
- Day 2 (Friday, 25.10, 9-11 AM): Module 5: GIT, String Formatting, Video Creation, Notebook structure
- Day 3 (Friday, 1.11, 9-11 AM): Module 6: Interpolation, Fitting, Complex Fitting, Filtering, Data analysis example
- Day 4 (Friday, 8.11, 9-11 AM): Module 7: Creating Files, Generators, Parallelization, Sympy, Integration of Plots to Overleaf
Pre-work and Preparation
Before the live sessions begin, participants are expected to complete the four self-learning modules listed above. These modules are provided as online videos and should be watched in advance.
Participants should allocate approximately 2-3 hours for each self-learning module, including time to practice the concepts introduced.
Course Progression
The course follows a blended learning approach, combining self-paced learning with live tutorials:
1. Self-learning modules (completed before live sessions)
2. Four live tutorial sessions (2 hours each, once per week)
3. Practice exercises and assignments between sessions
Time Commitment
Participants should expect to invest approximately 6 hours per week:
- 2 hours for the live tutorial session
- 3-4 hours for exercises, assignments, and additional practice
Course Materials
Participants will receive:
- Access to online video lectures for self-learning modules
- Jupyter notebooks with code examples and exercises
- Additional resources and reading materials
Prerequisites
This is not a beginner's course as we skip the fundamentals. Participants should have:
- Solid programming experience in any language, ideally Python
- Familiarity with mathematical concepts
- Access to a computer with Python and JupyterLab installed (see our YouTube channel for installation instructions)
Additional Resources
- For more detailed information about the course content and structure, please visit our website at [https://training-scientists.com]
- Check out our YouTube channel [https://www.youtube.com/@TrainingScientists](https://www.youtube.com/@TrainingScientists) for:
- Comparison of Jupyter Lab, VS Code and other IDEs
- Comparisons between ChatGPT, Claude, and GitHub Copilot
- Insights on AI tools in scientific computing
- Installation instructions for Python, JupyterLab, and necessary libraries
For any questions or clarifications, please contact the trainer Maurice@Training-Scientists.de
