SciPy
Optimisation, integration, statistics and signal processing.
SciPy is taught here in 3 lessons, running from SciPy arrays and NumPy interop through to Statistics and signal processing. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.
| Track | Python & Data Science |
| Lessons | 3 |
| Level | Beginner |
| Reading time | about 39 minutes |
| Prerequisites | Helpful, but not required: Django |
Lessons
- SciPy arrays and NumPy interopHow SciPy relates to NumPy, what it adds on top of the array type, and how to keep a large problem sparse instead of dense.
- Optimisation and curve fittingminimize for general problems, curve_fit for a model fitted to data, and how to tell a real solution from a local one.
- Statistics and signal processingscipy.stats for distributions, fitting and hypothesis tests; scipy.signal for filter design, zero-phase filtering and peak detection.
More in Python & Data Science
Python 3 NumPy pandas Matplotlib Jupyter Notebook Flask FastAPI Django Pillow Python 2.x R Julia
FAQ
How long does the SciPy course take?
It has 3 lessons, about 39 minutes of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for SciPy?
Not strictly. It helps to have read Django first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after SciPy?
Continue with Pillow (3 lessons), the next course in Python & Data Science.
Last refreshed 2026-09-18.