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Learning by doing
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Trainers with practical experience
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Classroom training
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Detailed course material
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Clear content description
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Tailormade content possible
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Training that proceeds
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Small groups
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 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 Bézier 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 |
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| 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 |
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.
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.
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