SciPy is an open source Python library for scientific, mathematical, and technical computing. It is built on the NumPy extension, providing a wide range of functionalities for performing complex numerical operations.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use SciPy to create advanced scientific computing functions with Python.
By the end of this training, participants will be able to:
- Set up the necessary development environment to start creating scientific computing functions.
- Get the full benefit of SciPy features by performing practical examples of complex operations.
- Implement and optimize mathematical algorithms and functions to solve scientific problems.
- Design data structures and interpolation methods for visualization, processing, and analysis.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- Python programming experience
- SciPy vs NumPy
- Overview of SciPy features and components
- 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
Summary and Next Steps