Data Engineering Labs

Project exercises from the Data Engineering course at AGH University of Kraków.

Six self-contained mini-projects, each in its own folder with a Jupyter notebook (projectNN.ipynb) and an equivalent script (projectNN.py).

Topics covered

#	Topic
project01	CSV / JSON / pickle parsing, basic pandas operations, column-level analysis
project02	Data import with encoding handling, field extraction, parsing edge cases
project03	Data cleaning, JSON merging, null handling
project04	Spatial / GIS data (coordinate systems, geographic visualizations)
project05	Time series analysis (resampling, rolling stats, decomposition)
project06	SQLite with window functions — running totals, lag, ranks — the most "engineering" of the bunch

How to run

cd projectNN
jupyter notebook projectNN.ipynb
# or run the script equivalent
python projectNN.py

The notebooks are self-contained and generate any intermediate output files (JSON, CSV, pickle) at runtime; those outputs are not committed.

Stack

python · pandas · numpy · jupyter · sqlite3 · spatial libraries for project04 · matplotlib / seaborn for plots.

A note

This is coursework — straightforward exercises rather than production work. Code prioritizes correctness and learning over abstraction.