SciPy Course

SciPy is where NumPy's arrays turn into actual scientific computing — optimization, statistics, linear algebra, and interpolation, all built on the array machinery you already know. This course covers the handful of sub-packages you'll reach for most often, each with real code and the exact numbers it produces.

What you'll learn

SciPy isn't one flat library — it's a collection of sub-packages, each focused on a different area of numerical computing, and you only import the ones you need. This course walks through five of the most commonly used: scipy.constants for physical constants and unit conversions, scipy.optimize for finding where a function is smallest, scipy.stats for distributions and descriptive statistics, scipy.linalg for solving systems of equations, and scipy.interpolate for filling in values between known data points.

This course assumes you're already comfortable with basic Python — variables, functions, lists — the kind of thing covered in the Python course. Every example is real, runnable code paired with the exact values it produces, so you can follow along in your own interpreter.

Lessons

  1. Introduction
  2. Constants & Basic Utilities
  3. Optimization
  4. Statistics
  5. Linear Algebra
  6. Interpolation
Where to start: begin with Introduction — it explains SciPy's sub-package structure, which every later lesson assumes you understand. After that, the lessons are largely independent of each other, so feel free to jump straight to whichever sub-package you actually need.