5.0 KiB
5.0 KiB
title, short_desc, tags, timestamp, path, public, update, editor, uuid, feature
| title | short_desc | tags | timestamp | path | public | update | editor | uuid | feature | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Time Table - Note |
|
24.09.2025 - 11:51 | true | true | markdown | 1758707464213 | thumbnails/external/89b30361e78c63dafa937dce92b1f550.svg |
Time Table
Every Friday from 15-16.30h
| Datum | Thema |
|---|---|
| 24.10.2025 | Organisation |
| 31.10.2025 | entfällt |
| 07.11.2025 | Tutorial 1 - Printing, Datatypes & Variables, Sequentials, Functions, Conditionals, Conditional Loops, Sequential Loops |
| 14.11.2025 | Tutorial 2 - Hash Tables, Error Handling, System Interactions, Modularization, Dataclasses |
| Documents/Arbeit/IFN/Programmieren WiSe 25 26/Vorlesungen/21.11.2025 | Extended Applications - Functions, Generators, Dataclasses, Built-In Modules, Syntax Styling PEP8, Working with AI |
| Documents/Arbeit/IFN/Programmieren WiSe 25 26/Vorlesungen/28.11.2025 | Matplotlib - Plotting basics NumPy - Multidimensional Arrays, Random Numbers, Efficient Computing |
| 05.12.2025 | SciPy - Distributions, Generating & Sampling Data |
| 12.12.2025 | Simulation - Monte Carlo, Generating Data |
| 19.12.2025 | Pandas - Dataframes, Series, Dataclasses |
| 26.12.2025 | entfällt |
| 02.01.2026 | entfällt |
| 09.01.2026 | Statistical Test Methods - T-Test, Correlations |
| 16.01.2026 | Data Analysis - Demo Project |
| 23.01.2026 | Folium - Maps, Markers, HTML |
| 30.01.2026 | Data Analysis - Demo Project |
| 06.02.2026 | Projects |
| 19.02.2026 | Prüfung (Termin unter vorbehalt) |
| 20.02.2026 | Prüfung (Termin unter vorbehalt) |
Ideen:
- Stock Market Simulation (Animal Crossing)
- Gini Index 938-828 791-748
gain_week = [
rand.uniform(-2.5, 2.5, sims)
for _ in range(7)
]
duration = np.zeros(sims)
for gain in gain_week:
duration += gain
duration += men
gain_percent = float(
np.round((duration < avg_weight-3).sum()/sims, decimals=2)
)
plt.figure(figsize=(10,5))
plt.hist(duration, density=True)
plt.axvline(avg_weight-3, color='r')
plt.show()
print(gain_percent)
\text{Birth Rate} = \frac{B}{P} * 1000