(in Polish) Introduction to scientific data analysis and visualization with Python 0800-SCIEDATA
The course will introduce students to using Python and interactive Jupyter Notebooks (https://docs.jupyter.org/en/latest/) to analyze and visualize different types of scientific data (e.g. data measured during laboratory work, or calculated based on various physical models).
Preliminary course schedule (computer laboratories):
1. Introduction: Types of scientific data
2. Introduction to Python and Jupyter Notebooks
3. Introduction to Python (continued): Numpy and Matplotlib
4. Visualizing one-variable functions
5. Visulizing one-variable functions (continued)
6. Visualizing two-variable functions
7. Visualizing two-variable functions (continued)
8. Visualizing multi-dimensional data: isosurfaces
9. Visualizing multi-dimensional data: band structure
10. Working with large datasets: Pandas
11. Working with large datasets: Pandas (continued)
12. Course projects
13. Statistical analysis
14. Estimation of errors
15. Project work
Total student workload
Learning outcomes - knowledge
Learning outcomes - skills
Learning outcomes - social competencies
Teaching methods
Expository teaching methods
- participatory lecture
Exploratory teaching methods
- practical
- laboratory
Type of course
Prerequisites
Course coordinators
Assessment criteria
The final grade will be a weighted average of grades assigned for (1) the active participation in classes 20%, (2) homework (30%), (3) Jupyter Notebook of the final project (25%), (4) final project presentation (25%).
• 0 - 49%: grade 2
• 50% - 60%: grade 3
• 61% - 70%: grade 3+
• 71% - 80%: grade 4
• 81% - 90%: grade 4+
• 91% - 100%: grade 5.
Bibliography
https://docs.jupyter.org/en/latest/
https://www.w3schools.com/python/numpy/
https://www.w3schools.com/python/matplotlib_intro.asp
https://www.w3schools.com/python/pandas/
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: