Scientific Python

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

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.

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.

Modules

Module 1 : SciPy Intro

• 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

Module 2 : Matrix Calculations

• 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

Module 3 : Nonlinear Equations

• 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

• Plane geometry
• Static problems
• Convex hulls
• Voronoi diagrams
• Triangulations
• Shortest paths
• Geometric query problems
• Point location
• Nearest neighbors
• Range searching
• Dynamic problems
• Bézier curves

Module 5 : Descriptive Statistics

• 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

Module 6 : Inference and Data Analysis

• 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