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In this course the participants will learn what can be done with the Python SciPy library for scientific computing.
The course starts with an overview of the role of matrices to solve problems in scientific computing.
Next the course proceeds by reviewing basic manipulation and operations on them, followed by factorizations, solutions of matrix equations, and the computation of eigenvalues and eigenvectors.
Also interpolation and approximation is treated where advanced techniques are shown to approximate functions and their applications in scientific computing.
Differentiation techniques to produce derivatives of functions are discussed as well as integration techniques showing how to compute areas and volumes effectively.
The module Computational Geometry takes a tour of the most significant algorithms in this branch of computer science.
And finally the course pays attention to statistical inference, machine learning, and data mining.
Scientists, mathematicians, engineers and others who want to use the SciPy Python library to create applications and perform data analysis.
Knowledge of Python programming and the NumPy library is required. Some knowledge of numerical methods in scientific computing is beneficial for the understanding.
The theory is dealt with on the basis of presentation slides. The concepts are illustrated with demos. The theory is interspersed with exercises. The course times are from 9.30 to 16.30.
The participants get well after completion of the course, an official certificate Scientific Python.
Module 1 : SciPy Intro
Module 2 : Matrix Calculations
Module 3 : Nonlinear Equations
|What is SciPy
Installing SciPy stack
Using ndarray class
Using matrix class
Traces and determinants
Transposes and inverses
|Singular value decomposition
Differentiation and Integration
|Non-linear equations and systems
Simple iterative solvers
The Broyden method
Powell's hybrid solver
Module 4 : Computational Geometry
Module 5 : Descriptive Statistics
Module 6 : Inference and Data Analysis
Geometric query problems
Scatterplots and correlation
Analysis of the time series
Estimation of parameters
Trees and Naive Bayes
Gaussian mixture models
Module 7 : Mathematical Imaging
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