Course Scientific Python

  • Content
  • Training
  • Modules
  • General
  • Reviews
  • Certificate
  • Course Scientific Python : Content

    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.

  • 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
    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
    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
    Shortest paths
    Geometric query problems
    Point location
    Nearest neighbors
    Range searching
    Dynamic problems
    B├ęzier curves
    Symbolic setting
    Numerical setting
    Data exploration
    Picturing distributions
    Bar plots
    Pie charts
    Time plots
    Scatterplots and correlation
    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
    Alpha channels
    Smoothing filters
    Multivariate calculus
    Statistical filters
    Fourier analysis
    Wavelet decompositions
    Image compression
    Image editing
    Rescale and resize
    Image restoration
    Noise reduction
  • Course Scientific Python : General

    Course Forms

    All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.


    We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.

    Course times

    The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.


    We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.


    Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.


    The course includes lunch that we use in a restaurant within walking distance of the course room.


    The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.


    At the end of each course, participants are requested to evaluate the course in terms of course content, course material, trainer and location. The evaluation form can be found at https://www.klantenvertellen.nl/reviews/1039545/spiraltrain?lang=en. The evaluations of previous participants and previous courses can also be found there.


    The intellectual property rights of the published course content, also referred to as an information sheet, belong to SpiralTrain. It is not allowed to publish the course information, the information sheet, in written or digital form without the explicit permission of SpiralTrain. The course content is to be understood as the description of the course content in sentences as well as the division of the course into modules and topics in the modules.

  • Course Scientific Python : Reviews

  • Course Scientific Python : Certificate