🛠️ Projects¶
9 hands-on data-analytics projects covering NumPy, Matplotlib, Pandas, and a capstone NLP dashboard. Each project has its own folder with main.py (or app.py) and README.md.
Difficulty Scale¶
| Rating | Level |
|---|---|
| ⭐ | Beginner |
| ⭐⭐ | Beginner–Intermediate |
| ⭐⭐⭐ | Intermediate–Advanced |
Project Index¶
| # | Folder | Topic Areas | Difficulty |
|---|---|---|---|
| 01 | 04-01-numpy-array-explorer | NumPy basics, arange, linspace, vectorised ops | ⭐⭐ |
| 02 | 04-02-student-score-analyzer-numpy | NumPy 2D, axis ops, np.where, normalise | ⭐⭐⭐ |
| 03 | 04-03-city-statistics-matplotlib | Matplotlib bar/barh, subplots, annotations | ⭐⭐ |
| 04 | 04-04-stock-price-chart | line plots, moving average, annotate, diff | ⭐⭐⭐ |
| 05 | 04-05-distribution-analyzer | histograms, axvline, axvspan, density | ⭐⭐ |
| 06 | 04-06-employee-analytics-pandas | Pandas basics, groupby, apply, CSV I/O | ⭐⭐ |
| 07 | 04-07-sales-dashboard | pd.merge, groupby, named agg, pie chart | ⭐⭐⭐ |
| 08 | 04-08-city-demographics-explorer | pivot_table, melt, concat, .str methods | ⭐⭐⭐ |
| 09 | 04-09-rl-book-word-frequency-analyzer | NLP, Streamlit, NLTK, Counter, visualisation | ⭐⭐⭐ |
Topic Coverage Map¶
| Topic | Projects |
|---|---|
| NumPy | 01, 02 |
| Matplotlib | 03, 04, 05 |
| Pandas | 06, 07, 08 |
| Comprehensive / NLP | 09 |
How to Use¶
- Open any project folder
- Read its
README.mdto understand what it does - Run
python main.py(project 09 runs withstreamlit run app.py) - Read through the source — every function has docstrings and inline comments
- Modify and extend the project to practise further
Tip
New to Python? Start with the companion Python Fundamentals course first.