Learning by doing
Trainers with practical experience
Detailed course material
Clear content description
Tailormade content possible
Training that proceeds
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
Rescale and resize
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.