(in Polish) Scientific Data Analysis and Visualization in Python 7404-SCI_DATA
Preliminary course schedule (computer laboratories):
1. Introduction: scientific data used in research and ways to visualize it
2. Data visualization with Matplotlib
3. Visualizing one-variable functions
4. One-variable functions: fitting, processing, best practices
5. Project 1: visulizing one-variable functions
6. Data and metadata
7. Best practices in data visualization
8. Two-variable functions
9. Project 2: visualizing two-variable functions
10. Multi-dimensional data: isosurfaces, one and two-diomensional surface cuts
11. Working with large datasets: Pandas
12. Course project selection
13. Statistical analysis and estimation of errors
14. Project 3: statistical analysis on a large dataset
15. Final project work
Total student workload
Learning outcomes - knowledge
Learning outcomes - skills
Learning outcomes - social competencies
Teaching methods
Exploratory teaching methods
- 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) howeworks (30%), (3) Final project (50%).
• 0 - 49%: grade 2
• 50% - 60%: grade 3
• 61% - 70%: grade 3+
• 71% - 80%: grade 4
• 81% - 90%: grade 4+
• 91% - 100%: grade 5.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: