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Course Scientific Python

In this course the participants will learn what can be done with the Python SciPy library for scientific computing.

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    General
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  • Course Scientific Python : Content

    Matrices in Science

    The course starts with an overview of the role of matrices to solve problems in scientific computing.

    Matrix Manipulation

    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.

    Interpolation and Approximation

    Also interpolation and approximation is treated where advanced techniques are shown to approximate functions and their applications in scientific computing.

    Differentiation en Integration

    Differentiation techniques to produce derivatives of functions are discussed as well as integration techniques showing how to compute areas and volumes effectively.

    Computational Geometry

    The module Computational Geometry takes a tour of the most significant algorithms in this branch of computer science.

    Statistics and Data Mining

    And finally the course pays attention to statistical inference, machine learning, and data mining.

  • Course Scientific Python : Training

    Audience Scientific Python Course

    Scientists, mathematicians, engineers and others who want to use the SciPy Python library to create applications and perform data analysis.

    Prerequisites Course Scientific Python

    Knowledge of Python programming and the NumPy library is required. Some knowledge of numerical methods in scientific computing is beneficial for the understanding.

    Realization Training Scientific Python

    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.

    Certification Course Scientific Python

    The participants get well after completion of the course, an official certificate Scientific Python.

    Course Scientific Python
  • Course Scientific Python : Modules

    Module 1 : SciPy Intro

    Module 2 : Matrix Calculations

    Module 3 : Nonlinear Equations

    What is SciPy
    Installing SciPy stack
    Anaconda distribution
    Constructing matrices
    Using ndarray class
    Using matrix class
    Sparse matrices
    Linear operators
    Scalar multiplication
    Matrix addition
    Matrix multiplication
    Traces and determinants
    Transposes and inverses
    Singular value decomposition
    Matrix equations
    Least squares
    Spectral decomposition
    Interpolations
    Univariate interpolation
    Nearest-neighbors interpolation
    Other interpolations
    Differentiation and Integration
    Numerical differentiation
    Symbolic differentiation
    Symbolic integration
    Numerical integration
    Non-linear equations and systems
    Iterative methods
    Bracketing methods
    Secant methods
    Brent method
    Simple iterative solvers
    The Broyden method
    Powell's hybrid solver
    Large-scale solvers
    Optimization
    Unconstrained optimization
    Constrained optimization
    Stochastic methods

    Module 4 : Computational Geometry

    Module 5 : Descriptive Statistics

    Module 6 : Inference and Data Analysis

    Plane geometry
    Static problems
    Convex hulls
    Voronoi diagrams
    Triangulations
    Shortest paths
    Geometric query problems
    Point location
    Nearest neighbors
    Range searching
    Dynamic problems
    B├ęzier curves
    Probability
    Symbolic setting
    Numerical setting
    Data exploration
    Picturing distributions
    Bar plots
    Pie charts
    Histograms
    Time plots
    Scatterplots and correlation
    Regression
    Analysis of the time series
    Statistical inference
    Estimation of parameters
    Bayesian approach
    Likelihood approach
    Interval estimation
    Frequentist approach
    Bayesian approach
    Likelihood approach
    Data mining
    Machine learning
    Trees and Naive Bayes
    Gaussian mixture models

    Module 7 : Mathematical Imaging

    Digital images
    Binary
    Gray-scale
    Color
    Alpha channels
    Smoothing filters
    Multivariate calculus
    Statistical filters
    Fourier analysis
    Wavelet decompositions
    Image compression
    Image editing
    Rescale and resize
    Swirl
    Image restoration
    Noise reduction
  • Course Scientific Python : General

    Read general course information
  • Course Scientific Python : Reviews

  • Course Scientific Python : Certificate