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SciPy

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. 1

    Introduction to SciPy

    What SciPy adds on top of NumPy.

  2. 2

    Statistics Module

    Distributions and hypothesis tests.

  3. 3

    Optimization Basics

    Minimizing a function with scipy.optimize.

  4. 4

    Linear Algebra

    Matrix operations with scipy.linalg.

  5. 5

    Interpolation

    Estimating values between known data points.

  6. 6

    Signal Processing Basics

    Filtering and analyzing signals.

  7. 7

    Sparse Matrices

    Efficiently storing mostly-zero matrices.

  8. 8

    Integration

    Numerical integration with scipy.integrate.

Start learning — it's free →

SciPy is free to study, self-paced. Pass the final exam to earn an optional Certificate of Completion for $40.