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Intermediate 4h 05min 11 lessons · 12 pages
Numerical Python (NumPy)
Unlock high-performance vectorized math. NumPy is the engine that powers Pandas, Scikit-Learn, and all matrix operations.
Start ModuleWelcome to NumPy 🔢
NumPy arrays are 10-100x faster than Python lists for numerical computing. NumPy is the foundation of the entire data science ecosystem.
Why NumPy?
- Vectorized operations - Apply math to entire arrays instantly
- Memory efficient - Store millions of numbers compactly
- Broadcasting - Operations across different shapes
- Linear algebra - Matrix multiplication, eigenvalues, solving systems
- Random numbers - Simulations and statistical testing
Real-World Speed
Doubliing 1,000,000 numbers:
- Python list with loop: ~100ms
- NumPy array (vectorized): ~1ms
That's 100x faster!
Prerequisites
✅ Module 1 (Python Basics)
Let's unlock numerical superpower! ⚡
Curriculum
1
Arrays vs Lists: Why NumPy?
Understand vectorization and create your first n-dimensional arrays.
Intermediate
2
Broadcasting & Linear Algebra
Perform matrix math, dot products, and understand broadcasting rules.
Advanced
3
Universal Functions (ufuncs)
Replace slow Python loops with blazing-fast C-level element-wise operations.
Intermediate
4
Statistical Operations
Calculate means, variances, standard deviations, and percentiles along specific axes.
Intermediate