Tool
SciPy
Scientific computing built on top of NumPy
Learn SciPy's statistics, optimization, and linear algebra tools.
8
Chapters
17
Hours (estimated)
1
Self-paced
Part-time
About 6 weeks
1 hour a day, 3 days a week
Full-time
About 2 weeks
3 hours a day, 3 days a week
Estimated from 8 chapters (notes, practice, labs, quiz, exercise, and challenge) plus the final exam. Go at your own pace.
Curriculum
8 chapters, each with a tutorial, a live example, a quiz, an exercise, a challenge, and class practice.
- 1
Introduction to SciPy
What SciPy adds on top of NumPy.
- 2
Statistics Module
Distributions and hypothesis tests.
- 3
Optimization Basics
Minimizing a function with scipy.optimize.
- 4
Linear Algebra
Matrix operations with scipy.linalg.
- 5
Interpolation
Estimating values between known data points.
- 6
Signal Processing Basics
Filtering and analyzing signals.
- 7
Sparse Matrices
Efficiently storing mostly-zero matrices.
- 8
Integration
Numerical integration with scipy.integrate.
SciPy is free to study, self-paced. Pass the final exam to earn an optional Certificate of Completion for $40.