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
Testimonials (5)
The trainer showed that he has a good understanding of the subject.
Marino - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
flexibility of approach to the client. the trainer was able to prepare issues that were of interest to the training participants.
Mirosław - CREDIT SUISSE (POLAND)
Course - Python Programming - 4 days
Machine Translated
Plenty of examples - and the trainer willing to bend backwards to help us with topics we were weaker in.
Wei Lit Teoh - HP Singapore (Private) Ltd.
Course - Advanced Python - 4 Days
The accesibilit of the trainer and the ability to communicate very effective,
Ciprian Ilie - Institutul National de Sanatate Publica
Course - Programming for Biologists
I did like the exercises.