Course Outline

Introduction

  • SciPy vs NumPy
  • Overview of SciPy features and components

Getting Started

  • Installing SciPy
  • Understanding basic functions

Implementing Scientific Computing

  • Using SciPy constants
  • Calculating integrals
  • Solving linear equations
  • Creating matrices with sparse and graphs
  • Optimizing or minimizing functions
  • Performing significance tests
  • Working with different file formats (Matlab, IDL, Matrix Market, etc.)

Visualizing and Manipulating Data

  • Implementing K-means clustering
  • Using spatial data structures
  • Processing multidimensional images
  • Calculating Fourier transformations
  • Using interpolation for fixed data points

Troubleshooting

Summary and Next Steps

Requirements

  • Python programming experience

Audience

  • Developers
 7 Hours

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