Week 3
NumPy Essentials
Arrays, vectorized operations, shapes, indexing, and numerical thinking.
Learning goals
- Why NumPy matters
- Creating arrays
- Indexing and slicing
- Shape, reshape, transpose
- Math on arrays
Suggested classroom flow
| Session | Focus | Output |
|---|---|---|
| Day 1 | Concept introduction and guided examples | Notebook or script |
| Day 2 | Hands-on lab and teacher walkthrough | Working code |
| Day 3 | Practice tasks and discussion | Assignment draft |
| Day 4 | Assessment and mini project | Submission |
Mini project
Array Lab Visual Notes
Outcome for the week: students should finish with usable code, a short explanation, and at least one visual or result artifact.
Quick MCQ preview
Q1. NumPy is mainly used for?
Explanation: NumPy is a numerical computing library.
Q2. Which attribute gives array dimensions?
Explanation: shape returns dimensions.
Q3. Vectorized operations are usually?
Explanation: Vectorization is usually faster.