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Numerical Methods in Python Programming

Numerical Methods in Python Programming Learn the workings of the most common numerical methods and a step by step process on how to program each of them
Numerical Methods in Python Programming Learn the workings of the most common numerical methods and a step by step process on how to program each of them

Numerical Methods in Python Programming

Learn the workings of the most common numerical methods and a step by step process on how to program each of them

What you’ll learn

Numerical Methods in Python Programming

  • Approximate integrals using Trapezoidal rule, Simpson’s 1/3 rule, and Romberg integration
  • Find roots of equations using bisection, False position, newton Raphson, and secant methods
  • Find analytically the optimum min and max of a function
  • Solve Ordinary Differential Equations using Runge Kutta Methods (i.e. Euler, Heun’s, Midpoint, and Ralston Methods in addition to fourth-order Runge Kutta Method
  • Find numerically the optimum min and max using Golden section Search method, newton Raphson Technique, and finally the gradient descent/ascent method
  • Solve Systems of Equations using Gauss elimination
  • Perform curve fitting using regression analysis including linear and polynomial regression in addition to linearization for fitting more complex functions

Requirements

  • Computer & Access to Microsoft Excel
  • Knowledge of basic Algebra, Geometry & Calculus Concepts
  • Knowledge of basic Python Programming

Description

Numerical modeling is a very powerful branch of mathematics. It’s capable to solve very complex problems using very simple techniques.

It is a branch that can differentiate and integral without the need to use any of the sometimes complex differentiation and integration rules. It can create best fit models with just knowing a data set. And create functions where the only thing we know is its derivative and a condition. And best of all, it can generate approximations that have such a low percentage error that they are as good as the true value.

But…

There is a limitation to numerical methods. They depend on iterative calculations. If for example, you want an approximation with a low error, for example, 0.001%, this will require a large number of calculations which can be sometimes impossible to do by hand not to mention tedious. This is where programming comes in.

In this course, I will walk you through not only the workings of each technique but a step-by-step process on how to program each of these techniques and perform hundreds if not thousands of calculations with a click of a button using one of the most popular programming languages – Python.

The great thing about programming languages is they all follow the same programming structure, sequence, repetition, and decision making. Meaning, if you know one language you can learn another very easily by just knowing how these structures are defined in the new language.

In this course, you’ll have a very good grasp of this structure so if you decide to learn another language afterward it will be very easy.

Who this course is for:

  • Students enrolled in their first numerical Methods Class and interested in additional mentoring
  • Who students interested to learn the most common Numerical Methods Techniques used in science and engineering
  • Students interested in understanding how to program and create Numerical Modeling Techniques
  • Last updated 8/2021

Numerical Methods in Python Programming

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