Data Science with Python

Pandas, NumPy, Matplotlib & real datasets

🎓 Requires basic Python (see our Python Bootcamp)⏱ 14 hours of content📄 PDF workbook🎥 Video lessons📱 Phone / laptop

Learn the daily toolkit of a working data analyst: pandas for tabular data, NumPy for numerical arrays, matplotlib and seaborn for visualisation. Every lesson uses a real South African dataset — property prices, load-shedding schedules, matric pass rates — so the skills transfer directly to the job market.

What's inside this pack

✓Comprehensive PDF workbook
✓9 hours of video lessons
✓10 real SA datasets (CSV)
✓30 Jupyter notebooks
✓Capstone project: end-to-end data report
✓Portfolio-ready GitHub template

Sample lessons — read before you buy

4 previews

1Lesson 1 — pandas DataFrames from Zero

import pandas as pd df = pd.read_csv('matric_results_2024.csv') df.head() # first 5 rows df.describe() # count, mean, std, min, max df['pass_rate'].mean() # single-column statistic Every data task starts with these three commands — they tell you the shape, types, and health of your dataset in under 2 seconds.

2Lesson 2 — Filtering, Grouping and Aggregating

# provinces with pass rate above 85% top = df[df['pass_rate'] > 85] # average pass rate per province by_prov = df.groupby('province')['pass_rate'].mean().sort_values(ascending=False) Groupby is the workhorse of analysis: split by a key, apply a function, combine the results. Learn it once and 80% of business questions become one-liners.

3Lesson 3 — Visualising with matplotlib & seaborn

import seaborn as sns sns.barplot(data=by_prov.reset_index(), x='province', y='pass_rate') plt.title('2024 Matric Pass Rate by Province') plt.xticks(rotation=45) plt.show() A good chart has a clear title, sorted categories (largest first), and no more than 3 colours. Everything else is decoration.

4Lesson 4 — Merging & Real-World Messy Data

joined = pd.merge(learners, results, on='school_id', how='left') Always specify 'how' — left / right / inner / outer — because the default (inner) silently drops rows that don't match on both sides. Real data is messy: strip and lowercase keys before merging (df['school_id'] = df['school_id'].str.strip().str.lower()) — this habit saves hours.

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