The large majority of mathematical modeling requires proper numerical software. We at Expert Analytics believe that the corner stone for the vast majority of such problems should be solved using Python. (Though in many cases it should be combined with programs written i C++, as addressed here.)

Course Content

In this course we will give an introduction to numerical Python. We will focus on computational speed and illustrate how to process large data structures using Python's minimal formulation. The course will also introduce some of the most popular numerical processing tools and visualize the results.

Prerequisite

This course requires some familiarity with the Python programming language, introduced here.

Duration: 2-4 hours

Tools introduced

Numpy

Numpy is the most popular tool for performing numerical operations in Python. The library consist of functions written in C, but with a Python explicit frontend. This gives Numpy computation speed comparable to buffered C, but the convenience of coding in Python. The Numpy syntax should be familiar to those experienced with Matlab.

Scipy

Scipy is a toolbox built on to Numpy and provide a vast collection of numerical optimization and processing tools. The toolbox contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Matplotlib

Matplotlib is the Scipy recommended library for plotting and visualization when using Numpy and Scipy. The library allows for easy production of publication quality figures in a variety of hardcopy formats and interactive environments across platforms.