NumPy Course

NumPy is the foundation almost every other Python data library is built on top of — pandas, scikit-learn, and even parts of TensorFlow all lean on it under the hood. It replaces Python's built-in lists with a single, fast, typed array structure, plus a set of operations that act on an entire array at once instead of one element at a time.

What you'll learn

You'll start by creating arrays a handful of different ways, then move through indexing and slicing (which looks like list syntax but behaves differently once you're in more than one dimension), element-wise operations and broadcasting, reshaping, and the aggregation functions you'll reach for constantly — sums, means, and axis-based totals. The course finishes with boolean filtering and a short introduction to the linear algebra operations that make NumPy the backbone of numerical Python.

This course assumes you already know basic Python — variables, lists, loops, and functions. If any of that feels shaky, the Python course covers it first.

Lessons

  1. Introduction
  2. Creating Arrays
  3. Indexing & Slicing
  4. Array Operations
  5. Reshaping Arrays
  6. Aggregations
  7. Boolean Indexing & Filtering
  8. Linear Algebra Basics
Where to start: begin with Introduction and go in order — later lessons (aggregations, boolean filtering, linear algebra) all build on the indexing and array-creation habits from the first few lessons.