NumPy Summary & Next Steps

Well done! You can now create, index, slice, reshape, join, filter, sort, and do maths on arrays — the everyday toolkit of data science.

What you have learned

• The ndarray and why it beats lists for numbers
• Creating arrays and controlling their shape and dtype
• Indexing, slicing, reshaping, joining, and splitting
• Boolean masks for searching and filtering
• Vectorised maths, aggregations, broadcasting, and random numbers

A quick mixed example

import numpy as np
scores = np.array([55, 80, 95, 60, 75, 88])
print("Average:", scores.mean())
print("Passed (>=60):", scores[scores >= 60])
print("Top score:", scores.max())

Where to go next

1. Pandas — built on NumPy, for real labelled data tables (our next course)
2. Machine Learning — NumPy arrays are the input to every model
3. Practise by re-creating spreadsheet calculations with arrays

💡 Keep going: NumPy plus Pandas is the core skill set of every data analyst and data scientist.